Creating a superset of knowledge

ABSTRACT

A method includes determining a set of identigens for words of content to produce a sets of identigens and interpreting the sets of identigens to determine a most likely meaning interpretation of the content and produce a baseline entigen group. The method further includes recovering an incomplete entigen group for the topic from a first knowledge database based on a knowledge defect of the incomplete entigen group with regards to the topic. The method further includes obtaining an additive entigen group from a second knowledge database based on the knowledge defect and modifying the incomplete entigen group utilizing the additive entigen group to produce an updated entigen group to provide a beneficial cure for the knowledge defect of the incomplete entigen group.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility patent application claims priority pursuant to35 U.S.C. § 120 as a continuation in part of U.S. Utility applicationSer. No. 16/385,516, entitled “INTERPRETING A MEANING OF A WORD STRING”filed Apr. 16, 2019, issuing May 3, 2022 as U.S. Pat. No. 11,321,530,which claims priority pursuant to 35 U.S.C. § 119(e) to U.S. ProvisionalApplication No. 62/660,127, entitled “PROCESSING CONTENT TO EXTRACTKNOWLEDGE,” filed Apr. 19, 2018, all of which are hereby incorporatedherein by reference in their entirety and made part of the present U.S.Utility patent application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

NOT APPLICABLE

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

NOT APPLICABLE

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computing systems and moreparticularly to generating data representations of data and analyzingthe data utilizing the data representations.

Description of Related Art

It is known that data is stored in information systems, such as filescontaining text. It is often difficult to produce useful informationfrom this stored data due to many factors. The factors include thevolume of available data, accuracy of the data, and variances in howtext is interpreted to express knowledge. For example, many languagesand regional dialects utilize the same or similar words to representdifferent concepts.

Computers are known to utilize pattern recognition techniques and applystatistical reasoning to process text to express an interpretation in anattempt to overcome ambiguities inherent in words. One patternrecognition technique includes matching a word pattern of a query to aword pattern of the stored data to find an explicit textual answer.Another pattern recognition technique classifies words into majorgrammatical types such as functional words, nouns, adjectives, verbs andadverbs. Grammar based techniques then utilize these grammatical typesto study how words should be distributed within a string of words toform a properly constructed grammatical sentence where each word isforced to support a grammatical operation without necessarilyidentifying what the word is actually trying to describe.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a computingsystem in accordance with the present invention;

FIG. 2 is a schematic block diagram of an embodiment of various serversof a computing system in accordance with the present invention;

FIG. 3 is a schematic block diagram of an embodiment of various devicesof a computing system in accordance with the present invention;

FIGS. 4A and 4B are schematic block diagrams of another embodiment of acomputing system in accordance with the present invention;

FIG. 4C is a logic diagram of an embodiment of a method for interpretingcontent to produce a response to a query within a computing system inaccordance with the present invention;

FIG. 5A is a schematic block diagram of an embodiment of a collectionsmodule of a computing system in accordance with the present invention;

FIG. 5B is a logic diagram of an embodiment of a method for obtainingcontent within a computing system in accordance with the presentinvention;

FIG. 5C is a schematic block diagram of an embodiment of a query moduleof a computing system in accordance with the present invention;

FIG. 5D is a logic diagram of an embodiment of a method for providing aresponse to a query within a computing system in accordance with thepresent invention;

FIG. 5E is a schematic block diagram of an embodiment of an identigenentigen intelligence (IEI) module of a computing system in accordancewith the present invention;

FIG. 5F is a logic diagram of an embodiment of a method for analyzingcontent within a computing system in accordance with the presentinvention;

FIG. 6A is a schematic block diagram of an embodiment of an elementidentification module and an interpretation module of a computing systemin accordance with the present invention;

FIG. 6B is a logic diagram of an embodiment of a method for interpretinginformation within a computing system in accordance with the presentinvention;

FIG. 6C is a schematic block diagram of an embodiment of an answerresolution module of a computing system in accordance with the presentinvention;

FIG. 6D is a logic diagram of an embodiment of a method for producing ananswer within a computing system in accordance with the presentinvention;

FIG. 7A is an information flow diagram for interpreting informationwithin a computing system in accordance with the present invention;

FIG. 7B is a relationship block diagram illustrating an embodiment ofrelationships between things and representations of things within acomputing system in accordance with the present invention;

FIG. 7C is a diagram of an embodiment of a synonym words table within acomputing system in accordance with the present invention;

FIG. 7D is a diagram of an embodiment of a polysemous words table withina computing system in accordance with the present invention;

FIG. 7E is a diagram of an embodiment of transforming words intogroupings within a computing system in accordance with the presentinvention;

FIG. 8A is a data flow diagram for accumulating knowledge within acomputing system in accordance with the present invention;

FIG. 8B is a diagram of an embodiment of a groupings table within acomputing system in accordance with the present invention;

FIG. 8C is a data flow diagram for answering questions utilizingaccumulated knowledge within a computing system in accordance with thepresent invention;

FIG. 8D is a data flow diagram for answering questions utilizinginterference within a computing system in accordance with the presentinvention;

FIG. 8E is a relationship block diagram illustrating another embodimentof relationships between things and representations of things within acomputing system in accordance with the present invention;

FIGS. 8F and 8G are schematic block diagrams of another embodiment of acomputing system in accordance with the present invention;

FIG. 8H is a logic diagram of an embodiment of a method for processingcontent to produce knowledge within a computing system in accordancewith the present invention;

FIGS. 8J and 8K are schematic block diagrams another embodiment of acomputing system in accordance with the present invention;

FIG. 8L is a logic diagram of an embodiment of a method for generating aquery response to a query within a computing system in accordance withthe present invention;

FIG. 9A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 9B is a logic diagram of an embodiment of a method for optimizingingestion parsing within a computing system in accordance with thepresent invention;

FIG. 10A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 10B is a logic diagram of an embodiment of a method for handing offa parsing process within a computing system in accordance with thepresent invention;

FIG. 11A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 11B is a logic diagram of an embodiment of a method for enhancingperformance of parsing an ingested phrase within a computing system inaccordance with the present invention;

FIG. 12A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 12B is a logic diagram of an embodiment of a method for classifyingprepositional phrases within a computing system in accordance with thepresent invention;

FIG. 13A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 13B is a logic diagram of an embodiment of a method for creating asuperset of knowledge within a computing system in accordance with thepresent invention;

FIGS. 13C-13E are schematic block diagrams of another embodiment of acomputing system illustrating another example of creating a superset ofknowledge within a computing system in accordance with the presentinvention;

FIGS. 14A, 14D, 14E, and 14F are schematic block diagrams of anotherembodiment of a computing system illustrating an example of a method forinterpreting a meaning of a word string in accordance with the presentinvention;

FIGS. 14B and 14C are data flow diagrams illustrating an example of amethod for interpreting a meaning of a word string within a computingsystem in accordance with the present invention;

FIG. 15A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 15B is a logic diagram of an embodiment of a method for extractingrelationship information from content within a computing system inaccordance with the present invention;

FIGS. 16A, 16C, 16E, and 16G are schematic block diagram of anotherembodiment of a computing system illustrating an example of a method forupdating a document utilizing trusted new information in accordance withthe present invention;

FIGS. 16B, 16D, 16F, and 16H are representations of entigen groupsillustrating an example of a method for updating a document utilizingtrusted new information within a computing system in accordance with thepresent invention;

FIG. 17A is a schematic block diagram of another embodiment of a contentingestion module within a computing system in accordance with thepresent invention.

FIG. 17B is a logic diagram of an embodiment of a method forsubstantiating accuracy of a document within a computing system inaccordance with the present invention;

FIG. 18A is a schematic block diagram of another embodiment of aninterpretation module within a computing system in accordance with thepresent invention; and

FIG. 18B is a logic diagram of an embodiment of a method forinterpreting a phrase within a computing system in accordance with thepresent invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a computingsystem 10 that includes a plurality of user devices 12-1 through 12-N, aplurality of wireless user devices 14-1 through 14-N, a plurality ofcontent sources 16-1 through 16-N, a plurality of transactional servers18-1 through 18-N, a plurality of artificial intelligence (AI) servers20-1 through 20-N, and a core network 24. The core network 24 includesat least one of the Internet, a public radio access network (RAN), andany private network. Hereafter, the computing system 10 may beinterchangeably referred to as a data network, a data communicationnetwork, a system, a communication system, and a data communicationsystem. Hereafter, the user device and the wireless user device may beinterchangeably referred to as user devices, and each of thetransactional servers and the AI servers may be interchangeably referredto as servers.

Each user device, wireless user device, transactional server, and AIserver includes a computing device that includes a computing core. Ingeneral, a computing device is any electronic device that cancommunicate data, process data, and/or store data. A further generalityof a computing device is that it includes one or more of a centralprocessing unit (CPU), a memory system, a sensor (e.g., internal orexternal), user input/output interfaces, peripheral device interfaces,communication elements, and an interconnecting bus structure.

As further specific examples, each of the computing devices may be aportable computing device and/or a fixed computing device. A portablecomputing device may be an embedded controller, a smart sensor, a smartpill, a social networking device, a gaming device, a cell phone, a smartphone, a robot, a personal digital assistant, a digital music player, adigital video player, a laptop computer, a handheld computer, a tablet,a video game controller, an engine controller, a vehicular controller,an aircraft controller, a maritime vessel controller, and/or any otherportable device that includes a computing core. A fixed computing devicemay be security camera, a sensor device, a household appliance, amachine, a robot, an embedded controller, a personal computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a cameracontroller, a video game console, a critical infrastructure controller,and/or any type of home or office computing equipment that includes acomputing core. An embodiment of the various servers is discussed ingreater detail with reference to FIG. 2. An embodiment of the variousdevices is discussed in greater detail with reference to FIG. 3.

Each of the content sources 16-1 through 16-N includes any source ofcontent, where the content includes one or more of data files, a datastream, a tech stream, a text file, an audio stream, an audio file, avideo stream, a video file, etc. Examples of the content sources includea weather service, a multi-language online dictionary, a fact server, abig data storage system, the Internet, social media systems, an emailserver, a news server, a schedule server, a traffic monitor, a securitycamera system, audio monitoring equipment, an information server, aservice provider, a data aggregator, and airline traffic server, ashipping and logistics server, a banking server, a financial transactionserver, etc. Alternatively, or in addition to, one or more of thevarious user devices may provide content. For example, a wireless userdevice may provide content (e.g., issued as a content message) when thewireless user device is able to capture data (e.g., text input, sensorinput, etc.).

Generally, an embodiment of this invention presents solutions where thecomputing system 10 supports the generation and utilization of knowledgeextracted from content. For example, the AI servers 20-1 through 20-Ningest content from the content sources 16-1 through 16-N by receiving,via the core network 24 content messages 28-1 through 28-N as AImessages 32-1 through 32-N, extract the knowledge from the ingestedcontent, and interact with the various user devices to utilize theextracted knowledge by facilitating the issuing, via the core network24, user messages 22-1 through 22-N to the user devices 12-1 through12-N and wireless signals 26-1 through 26-N to the wireless user devices14-1 through 14-N.

Each content message 28-1 through 28-N includes a content request (e.g.,requesting content related to a topic, content type, content timing, oneor more domains, etc.) or a content response, where the content responseincludes real-time or static content such as one or more of dictionaryinformation, facts, non-facts, weather information, sensor data, newsinformation, blog information, social media content, user daily activityschedules, traffic conditions, community event schedules, schoolschedules, user schedules airline records, shipping records, logisticsrecords, banking records, census information, global financial historyinformation, etc. Each AI message 32-1 through 32-N includes one or moreof content messages, user messages (e.g., a query request, a queryresponse that includes an answer to a query request), and transactionmessages (e.g., transaction information, requests and responses relatedto transactions). Each user message 22-1 through 22-N includes one ormore of a query request, a query response, a trigger request, a triggerresponse, a content collection, control information, softwareinformation, configuration information, security information, routinginformation, addressing information, presence information, analyticsinformation, protocol information, all types of media, sensor data,statistical data, user data, error messages, etc.

When utilizing a wireless signal capability of the core network 24, eachof the wireless user devices 14-1 through 14-N encodes/decodes dataand/or information messages (e.g., user messages such as user messages22-1 through 22-N) in accordance with one or more wireless standards forlocal wireless data signals (e.g., Wi-Fi, Bluetooth, ZigBee) and/or forwide area wireless data signals (e.g., 2G, 3G, 4G, 5G, satellite,point-to-point, etc.) to produce wireless signals 26-1 through 26-N.Having encoded/decoded the data and/or information messages, thewireless user devices 14-1 through 14-N and/receive the wireless signalsto/from the wireless capability of the core network 24.

As another example of the generation and utilization of knowledge, thetransactional servers 18-1 through 18-N communicate, via the corenetwork 24, transaction messages 30-1 through 30-N as further AImessages 32-1 through 32-N to facilitate ingesting of transactional typecontent (e.g., real-time crypto currency transaction information) and tofacilitate handling of utilization of the knowledge by one or more ofthe transactional servers (e.g., for a transactional function) inaddition to the utilization of the knowledge by the various userdevices. Each transaction message 30-1 through 30-N includes one or moreof a query request, a query response, a trigger request, a triggerresponse, a content message, and transactional information, where thetransactional information may include one or more of consumer purchasinghistory, crypto currency ledgers, stock market trade information, otherinvestment transaction information, etc.

In another specific example of operation of the generation andutilization of knowledge extracted from the content, the user device12-1 issues a user message 22-1 to the AI server 20-1, where the usermessage 22-1 includes a query request and where the query requestincludes a question related to a first domain of knowledge. The issuingincludes generating the user message 22-1 based on the query request(e.g., the question), selecting the AI server 20-1 based on the firstdomain of knowledge, and sending, via the core network 24, the usermessage 22-1 as a further AI message 32-1 to the AI server 20-1. Havingreceived the AI message 32-1, the AI server 20-1 analyzes the questionwithin the first domain, generates further knowledge, generates apreliminary answer, generates a quality level indicator of thepreliminary answer, and determines to gather further content when thequality level indicator is below a minimum quality threshold level.

When gathering the further content, the AI server 20-1 issues, via thecore network 24, a still further AI message 32-1 as a further contentmessage 28-1 to the content source 16-1, where the content message 28-1includes a content request for more content associated with the firstdomain of knowledge and in particular the question. Alternatively, or inaddition to, the AI server 20-1 issues the content request to another AIserver to facilitate a response within a domain associated with theother AI server. Further alternatively, or in addition to, the AI server20-1 issues the content request to one or more of the various userdevices to facilitate a response from a subject matter expert.

Having received the content message 28-1, the contents or 16-1 issues,via the core network 24, a still further content message 28-1 to the AIserver 20-1 as a yet further AI message 32-1, where the still furthercontent message 28-1 includes requested content. The AI server 20-1processes the received content to generate further knowledge. Havinggenerated the further knowledge, the AI server 20-1 re-analyzes thequestion, generates still further knowledge, generates anotherpreliminary answer, generates another quality level indicator of theother preliminary answer, and determines to issue a query response tothe user device 12-1 when the quality level indicator is above theminimum quality threshold level. When issuing the query response, the AIserver 20-1 generates an AI message 32-1 that includes another usermessage 22-1, where the other user message 22-1 includes the otherpreliminary answer as a query response including the answer to thequestion. Having generated the AI message 32-1, the AI server 20-1sends, via the core network 24, the AI message 32-1 as the user message22-1 to the user device 12-1 thus providing the answer to the originalquestion of the query request.

FIG. 2 is a schematic block diagram of an embodiment of the AI servers20-1 through 20-N and the transactional servers 18-1 through 18-N of thecomputing system 10 of FIG. 1. The servers include a computing core 52,one or more visual output devices 74 (e.g., video graphics display,touchscreen, LED, etc.), one or more user input devices 76 (e.g.,keypad, keyboard, touchscreen, voice to text, a push button, amicrophone, a card reader, a door position switch, a biometric inputdevice, etc.), one or more audio output devices 78 (e.g., speaker(s),headphone jack, a motor, etc.), and one or more visual input devices 80(e.g., a still image camera, a video camera, photocell, etc.). Theservers further include one or more universal serial bus (USB) devices(USB devices 1-U), one or more peripheral devices (e.g., peripheraldevices 1-P), one or more memory devices (e.g., one or more flash memorydevices 92, one or more hard drive (HD) memories 94, and one or moresolid state (SS) memory devices 96, and/or cloud memory 98). The serversfurther include one or more wireless location modems 84 (e.g., globalpositioning satellite (GPS), Wi-Fi, angle of arrival, time difference ofarrival, signal strength, dedicated wireless location, etc.), one ormore wireless communication modems 86-1 through 86-N (e.g., a cellularnetwork transceiver, a wireless data network transceiver, a Wi-Fitransceiver, a Bluetooth transceiver, a 315 MHz transceiver, a zig beetransceiver, a 60 GHz transceiver, etc.), a telco interface 102 (e.g.,to interface to a public switched telephone network), and a wired localarea network (LAN) 88 (e.g., optical, electrical), and a wired wide areanetwork (WAN) 90 (e.g., optical, electrical).

The computing core 52 includes a video graphics module 54, one or moreprocessing modules 50-1 through 50-N (e.g., which may include one ormore secure co-processors), a memory controller 56 and one or more mainmemories 58-1 through 58-N (e.g., RAM serving as local memory). Thecomputing core 52 further includes one or more input/output (I/O) deviceinterfaces 62, an input/output (I/O) controller 60, a peripheralinterface 64, one or more USB interfaces 66, one or more networkinterfaces 72, one or more memory interfaces 70, and/or one or moreperipheral device interfaces 68.

The processing modules may be a single processing device or a pluralityof processing devices where the processing device may further bereferred to as one or more of a “processing circuit”, a “processor”,and/or a “processing unit”. Such a processing device may be amicroprocessor, micro-controller, digital signal processor,microcomputer, central processing unit, field programmable gate array,programmable logic device, state machine, logic circuitry, analogcircuitry, digital circuitry, and/or any device that manipulates signals(analog and/or digital) based on hard coding of the circuitry and/oroperational instructions.

The processing module, module, processing circuit, and/or processingunit may be, or further include, memory and/or an integrated memoryelement, which may be a single memory device, a plurality of memorydevices, and/or embedded circuitry of another processing module, module,processing circuit, and/or processing unit. Such a memory device may bea read-only memory, random access memory, volatile memory, non-volatilememory, static memory, dynamic memory, flash memory, cache memory,and/or any device that stores digital information. Note that if theprocessing module, module, processing circuit, and/or processing unitincludes more than one processing device, the processing devices may becentrally located (e.g., directly coupled together via a wired and/orwireless bus structure) or may be distributedly located (e.g., cloudcomputing via indirect coupling via a local area network and/or a widearea network).

Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

Each of the interfaces 62, 66, 68, 70, and 72 includes a combination ofhardware (e.g., connectors, wiring, etc.) and may further includeoperational instructions stored on memory (e.g., driver software) thatare executed by one or more of the processing modules 50-1 through 50-Nand/or a processing circuit within the interface. Each of the interfacescouples to one or more components of the servers. For example, one ofthe IO device interfaces 62 couples to an audio output device 78. Asanother example, one of the memory interfaces 70 couples to flash memory92 and another one of the memory interfaces 70 couples to cloud memory98 (e.g., an on-line storage system and/or on-line backup system). Inother embodiments, the servers may include more or less devices andmodules than shown in this example embodiment of the servers.

FIG. 3 is a schematic block diagram of an embodiment of the variousdevices of the computing system 10 of FIG. 1, including the user devices12-1 through 12-N and the wireless user devices 14-1 through 14-N. Thevarious devices include the visual output device 74 of FIG. 2, the userinput device 76 of FIG. 2, the audio output device 78 of FIG. 2, thevisual input device 80 of FIG. 2, and one or more sensors 82.

The sensor may be implemented internally and/or externally to thedevice. Example sensors includes a still camera, a video camera, servomotors associated with a camera, a position detector, a smoke detector,a gas detector, a motion sensor, an accelerometer, velocity detector, acompass, a gyro, a temperature sensor, a pressure sensor, an altitudesensor, a humidity detector, a moisture detector, an imaging sensor, anda biometric sensor. Further examples of the sensor include an infraredsensor, an audio sensor, an ultrasonic sensor, a proximity detector, amagnetic field detector, a biomaterial detector, a radiation detector, aweight detector, a density detector, a chemical analysis detector, afluid flow volume sensor, a DNA reader, a wind speed sensor, a winddirection sensor, and an object detection sensor.

Further examples of the sensor include an object identifier sensor, amotion recognition detector, a battery level detector, a roomtemperature sensor, a sound detector, a smoke detector, an intrusiondetector, a motion detector, a door position sensor, a window positionsensor, and a sunlight detector. Still further sensor examples includemedical category sensors including: a pulse rate monitor, a heart rhythmmonitor, a breathing detector, a blood pressure monitor, a blood glucoselevel detector, blood type, an electrocardiogram sensor, a body massdetector, an imaging sensor, a microphone, body temperature, etc.

The various devices further include the computing core 52 of FIG. 2, theone or more universal serial bus (USB) devices (USB devices 1-U) of FIG.2, the one or more peripheral devices (e.g., peripheral devices 1-P) ofFIG. 2, and the one or more memories of FIG. 2 (e.g., flash memories 92,HD memories 94, SS memories 96, and/or cloud memories 98). The variousdevices further include the one or more wireless location modems 84 ofFIG. 2, the one or more wireless communication modems 86-1 through 86-Nof FIG. 2, the telco interface 102 of FIG. 2, the wired local areanetwork (LAN) 88 of FIG. 2, and the wired wide area network (WAN) 90 ofFIG. 2. In other embodiments, the various devices may include more orless internal devices and modules than shown in this example embodimentof the various devices.

FIGS. 4A and 4B are schematic block diagrams of another embodiment of acomputing system that includes one or more of the user device 12-1 ofFIG. 1, the wireless user device 14-1 of FIG. 1, the content source 16-1of FIG. 1, the transactional server 18-1 of FIG. 1, the user device 12-2of FIG. 1, and the AI server 20-1 of FIG. 1. The AI server 20-1 includesthe processing module 50-1 (e.g., associated with the servers) of FIG.2, where the processing module 50-1 includes a collections module 120,an identigen entigen intelligence (IEI) module 122, and a query module124. Alternatively, the collections module 120, the IEI module 122, andthe query module 124 may be implemented by the processing module 50-1(e.g., associated with the various user devices) of FIG. 3. Thecomputing system functions to interpret content to produce a response toa query.

FIG. 4A illustrates an example of the interpreting of the content toproduce the response to the query where the collections module 120interprets (e.g., based on an interpretation approach such as rules) atleast one of a collections request 132 from the query module 124 and acollections request within collections information 130 from the IEImodule 122 to produce content request information (e.g., potentialsources, content descriptors of desired content). Alternatively, or inaddition to, the collections module 120 may facilitate gathering furthercontent based on a plurality of collection requests from a plurality ofdevices of the computing system 10 of FIG. 1.

The collections request 132 is utilized to facilitate collection ofcontent, where the content may be received in a real-time fashion onceor at desired intervals, or in a static fashion from previous discretetime frames. For instance, the query module 124 issues the collectionsrequest 132 to facilitate collection of content as a background activityto support a long-term query (e.g., how many domestic airline flightsover the next seven days include travelers between the age of 18 and 35years old). The collections request 132 may include one or more of arequester identifier (ID), a content type (e.g., language, dialect,media type, topic, etc.), a content source indicator, securitycredentials (e.g., an authorization level, a password, a user ID,parameters utilized for encryption, etc.), a desired content qualitylevel, trigger information (e.g., parameters under which to collectcontent based on a pre-event, an event (i.e., content quality levelreaches a threshold to cause the trigger, trueness), or a timeframe), adesired format, and a desired timing associated with the content.

Having interpreted the collections request 132, the collections module120 selects a source of content based on the content requestinformation. The selecting includes one or more of identifying one ormore potential sources based on the content request information,selecting the source of content from the potential sources utilizing aselection approach (e.g., favorable history, a favorable security level,favorable accessibility, favorable cost, favorable performance, etc.).For example, the collections module 120 selects the content source 16-1when the content source 16-1 is known to provide a favorable contentquality level for a domain associated with the collections request 132.

Having selected the source of content, the collections module 120 issuesa content request 126 to the selected source of content. The issuingincludes generating the content request 126 based on the content requestinformation for the selected source of content and sending the contentrequest 126 to the selected source of content. The content request 126may include one or more of a content type indicator, a requester ID,security credentials for content access, and any other informationassociated with the collections request 132. For example, thecollections module 120 sends the content request 126, via the corenetwork 24 of FIG. 1, to the content source 16-1. Alternatively, or inaddition to, the collections module 120 may send a similar contentrequest 126 to one or more of the user device 12-1, the wireless userdevice 14-1, and the transactional server 18-1 to facilitate collectingof further content.

In response to the content request 126, the collections module 120receives one or more content responses 128. The content response 128includes one or more of content associated with the content source, acontent source identifier, security credential processing information,and any other information pertaining to the desired content. Havingreceived the content response 128, the collections module 120 interpretsthe received content response 128 to produce collections information130, where the collections information 130 further includes acollections response from the collections module 120 to the IEI module122.

The collections response includes one or more of transformed content(e.g., completed sentences and paragraphs), timing informationassociated with the content, a content source ID, and a content qualitylevel. Having generated the collections response of the collectionsinformation 130, the collections module 120 sends the collectionsinformation 130 to the IEI module 122. Having received the collectionsinformation 130 from the collections module 120, the IEI module 122interprets the further content of the content response to generatefurther knowledge, where the further knowledge is stored in a memoryassociated with the JET module 122 to facilitate subsequent answering ofquestions posed in received queries.

FIG. 4B further illustrates the example of the interpreting of thecontent to produce the response to the query where, the query module 124interprets a received query request 136 from a requester to produce aninterpretation of the query request. For example, the query module 124receives the query request 136 from the user device 12-2, and/or fromone or more of the wireless user device 14-2 and the transactionalserver 18-2. The query request 136 includes one or more of an identifier(ID) associated with the request (e.g., requester ID, ID of an entity tosend a response to), a question, question constraints (e.g., within atimeframe, within a geographic area, within a domain of knowledge,etc.), and content associated with the question (e.g., which may beanalyzed for new knowledge itself).

The interpreting of the query request 136 includes determining whetherto issue a request to the JET module 122 (e.g., a question, perhaps withcontent) and/or to issue a request to the collections module 120 (e.g.,for further background content). For example, the query module 124produces the interpretation of the query request to indicate to send therequest directly to the JET module 122 when the question is associatedwith a simple non-time varying function answer (e.g., question: “howmany hydrogen atoms does a molecule of water have?”).

Having interpreted the query request 136, the query module 124 issues atleast one of an JET request as query information 138 to the JET module122 (e.g., when receiving a simple new query request) and a collectionsrequest 132 to the collections module 120 (e.g., based on two or morequery requests 136 requiring more substantive content gathering). TheJET request of the query information 138 includes one or more of anidentifier (ID) of the query module 124, an ID of the requester (e.g.,the user device 12-2), a question (e.g., with regards to content foranalysis, with regards to knowledge minded by the AI server from generalcontent), one or more constraints (e.g., assumptions, restrictions,etc.) associated with the question, content for analysis of thequestion, and timing information (e.g., a date range for relevance ofthe question).

Having received the query information 138 that includes the JET requestfrom the query module 124, the JET module 122 determines whether asatisfactory response can be generated based on currently availableknowledge, including that of the query request 136. The determiningincludes indicating that the satisfactory response cannot be generatedwhen an estimated quality level of an answer falls below a minimumquality threshold level. When the satisfactory response cannot begenerated, the JET module 122 facilitates collecting more content. Thefacilitating includes issuing a collections request to the collectionsmodule 120 of the AI server 20-1 and/or to another server or userdevice, and interpreting a subsequent collections response 134 ofcollections information 130 that includes further content to producefurther knowledge to enable a more favorable answer.

When the JET module 122 indicates that the satisfactory response can begenerated, the JET module 122 issues an JET response as queryinformation 138 to the query module 124. The JET response includes oneor more of one or more answers, timing relevance of the one or moreanswers, an estimated quality level of each answer, and one or moreassumptions associated with the answer. The issuing includes generatingthe IEI response based on the collections response 134 of thecollections information 130 and the IEI request, and sending the IEIresponse as the query information 138 to the query module 124.Alternatively, or in addition to, at least some of the further contentcollected by the collections module 120 is utilized to generate acollections response 134 issued by the collections module 120 to thequery module 124. The collections response 134 includes one or more offurther content, a content availability indicator (e.g., when, where,required credentials, etc.), a content freshness indicator (e.g.,timestamps, predicted time availability), content source identifiers,and a content quality level.

Having received the query information 138 from the IEI module 122, thequery module 124 issues a query response 140 to the requester based onthe IEI response and/or the collections response 134 directly from thecollections module 120, where the collection module 120 generates thecollections response 134 based on collected content and the collectionsrequest 132. The query response 140 includes one or more of an answer,answer timing, an answer quality level, and answer assumptions.

FIG. 4C is a logic diagram of an embodiment of a method for interpretingcontent to produce a response to a query within a computing system. Inparticular, a method is presented for use in conjunction with one ormore functions and features described in conjunction with FIGS. 1-3,4A-4B, and also FIG. 4C. The method includes step 150 where acollections module of a processing module of one or more computingdevices (e.g., of one or more servers) interprets a collections requestto produce content request information. The interpreting may include oneor more of identifying a desired content source, identifying a contenttype, identifying a content domain, and identifying content timingrequirements.

The method continues at step 152 where the collections module selects asource of content based on the content request information. For example,the collections module identifies one or more potential sources based onthe content request information and selects the source of content fromthe potential sources utilizing a selection approach (e.g., based on oneor more of favorable history, a favorable security level, favorableaccessibility, favorable cost, favorable performance, etc.). The methodcontinues at step 154 where the collections module issues a contentrequest to the selected source of content. The issuing includesgenerating a content request based on the content request informationfor the selected source of content and sending the content request tothe selected source of content.

The method continues at step 156 where the collections module issuescollections information to an identigen entigen intelligence (IEI)module based on a received content response, where the IEI moduleextracts further knowledge from newly obtained content from the one ormore received content responses. For example, the collections modulegenerates the collections information based on newly obtained contentfrom the one or more received content responses of the selected sourceof content.

The method continues at step 158 where a query module interprets areceived query request from a requester to produce an interpretation ofthe query request. The interpreting may include determining whether toissue a request to the IEI module (e.g., a question) or to issue arequest to the collections module to gather further background content.The method continues at step 160 where the query module issues a furthercollections request. For example, when receiving a new query request,the query module generates a request for the IEI module. As anotherexample, when receiving a plurality of query requests for similarquestions, the query module generates a request for the collectionsmodule to gather further background content.

The method continues at step 162 where the IEI module determines whethera satisfactory query response can be generated when receiving therequest from the query module. For example, the IEI module indicatesthat the satisfactory query response cannot be generated when anestimated quality level of an answer is below a minimum answer qualitythreshold level. The method branches to step 166 when the IEI moduledetermines that the satisfactory query response can be generated. Themethod continues to step 164 when the IEI module determines that thesatisfactory query response cannot be generated. When the satisfactoryquery response cannot be generated, the method continues at step 164where the IEI module facilitates collecting more content. The methodloops back to step 150.

When the satisfactory query response can be generated, the methodcontinues at step 166 where the IEI module issues an IEI response to thequery module. The issuing includes generating the IEI response based onthe collections response and the IEI request, and sending the IEIresponse to the query module. The method continues at step 168 where thequery module issues a query response to the requester. For example, thequery module generates the query response based on the IEI responseand/or a collections response from the collections module and sends thequery response to the requester, where the collections module generatesthe collections response based on collected content and the collectionsrequest.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 5A is a schematic block diagram of an embodiment of the collectionsmodule 120 of FIG. 4A that includes a content acquisition module 180, acontent selection module 182, a source selection module 184, a contentsecurity module 186, an acquisition timing module 188, a contenttransformation module 190, and a content quality module 192. Generally,an embodiment of this invention presents solutions where the collectionsmodule 120 supports collecting content.

In an example of operation of the collecting of the content, the contentacquisition module 180 receives a collections request 132 from arequester. The content acquisition module 180 obtains content selectioninformation 194 based on the collections request 132. The contentselection information 194 includes one or more of content requirements,a desired content type indicator, a desired content source identifier, acontent type indicator, a candidate source identifier (ID), and acontent profile (e.g., a template of typical parameters of the content).For example, the content acquisition module 180 receives the contentselection information 194 from the content selection module 182, wherethe content selection module 182 generates the content selectioninformation 194 based on a content selection information request fromthe content acquisition module 180 and where the content acquisitionmodule 180 generates the content selection information request based onthe collections request 132.

The content acquisition module 180 obtains source selection information196 based on the collections request 132. The source selectioninformation 196 includes one or more of candidate source identifiers, acontent profile, selected sources, source priority levels, andrecommended source access timing. For example, the content acquisitionmodule 180 receives the source selection information 196 from the sourceselection module 184, where the source selection module 184 generatesthe source selection information 196 based on a source selectioninformation request from the content acquisition module 180 and wherethe content acquisition module 180 generates the source selectioninformation request based on the collections request 132.

The content acquisition module 180 obtains acquisition timinginformation 200 based on the collections request 132. The acquisitiontiming information 200 includes one or more of recommended source accesstiming, confirmed source access timing, source access testing results,estimated velocity of content update's, content precious, timestamps,predicted time availability, required content acquisition triggers,content acquisition trigger detection indicators, and a duplicativeindicator with a pending content request. For example, the contentacquisition module 180 receives the acquisition timing information 200from the acquisition timing module 188, where the acquisition timingmodule 188 generates the acquisition timing information 200 based on anacquisition timing information request from the content acquisitionmodule 180 and where the content acquisition module 180 generates theacquisition timing information request based on the collections request132. Having obtained the content selection information 194, the sourceselection information 196, and the acquisition timing information 200,the content acquisition module 180 issues a content request 126 to acontent source utilizing security information 198 from the contentsecurity module 186, where the content acquisition module 180 generatesthe content request 126 in accordance with the content selectioninformation 194, the source selection information 196, and theacquisition timing information 200. The security information 198includes one or more of source priority requirements, requester securityinformation, available security procedures, and security credentials fortrust and/or encryption. For example, the content acquisition module 180generates the content request 126 to request a particular content typein accordance with the content selection information 194 and to includesecurity parameters of the security information 198, initiates sendingof the content request 126 in accordance with the acquisition timinginformation 200, and sends the content request 126 to a particulartargeted content source in accordance with the source selectioninformation 196.

In response to receiving a content response 128, the content acquisitionmodule 180 determines the quality level of received content extractedfrom the content response 128. For example, the content acquisitionmodule 180 receives content quality information 204 from the contentquality module 192, where the content quality module 192 generates thequality level of the received content based on receiving a contentquality request from the content acquisition module 180 and where thecontent acquisition module 180 generates the content quality requestbased on content extracted from the content response 128. The contentquality information includes one or more of a content reliabilitythreshold range, a content accuracy threshold range, a desired contentquality level, a predicted content quality level, and a predicted levelof trust.

When the quality level is below a minimum desired quality thresholdlevel, the content acquisition module 180 facilitates acquisition offurther content. The facilitating includes issuing another contentrequest 126 to a same content source and/or to another content source toreceive and interpret further received content. When the quality levelis above the minimum desired quality threshold level, the contentacquisition module 180 issues a collections response 134 to therequester. The issuing includes processing the content in accordancewith a transformation approach to produce transformed content,generating the collections response 134 to include the transformedcontent, and sending the collections response 134 to the requester. Theprocessing of the content to produce the transformed content includesreceiving content transformation information 202 from the contenttransformation module 190, where the content transformation module 190transforms the content in accordance with the transformation approach toproduce the transformed content. The content transformation informationincludes a desired format, available formats, recommended formatting,the received content, transformation instructions, and the transformedcontent.

FIG. 5B is a logic diagram of an embodiment of a method for obtainingcontent within a computing system. In particular, a method is presentedfor use in conjunction with one or more functions and features describedin conjunction with FIGS. 1-3, 4A-4C, 5A, and also FIG. 5B. The methodincludes step 210 where a processing module of one or more processingmodules of one or more computing devices of the computing systemreceives a collections request from the requester. The method continuesat step 212 where the processing module determines content selectioninformation. The determining includes interpreting the collectionsrequest to identify requirements of the content.

The method continues at step 214 where the processing module determinessource selection information. The determining includes interpreting thecollections request to identify and select one or more sources for thecontent to be collected. The method continues at step 216 where theprocessing module determines acquisition timing information. Thedetermining includes interpreting the collections request to identifytiming requirements for the acquisition of the content from the one ormore sources. The method continues at step 218 where the processingmodule issues a content request utilizing security information and inaccordance with one or more of the content selection information, thesource selection information, and the acquisition timing information.For example, the processing module issues the content request to the oneor more sources for the content in accordance with the contentrequirements, where the sending of the request is in accordance with theacquisition timing information.

The method continues at step 220 where the processing module determinesa content quality level for received content area the determiningincludes receiving the content from the one or more sources, obtainingcontent quality information for the received content based on a qualityanalysis of the received content. The method branches to step 224 whenthe content quality level is favorable and the method continues to step222 when the quality level is unfavorable. For example, the processingmodule determines that the content quality level is favorable when thecontent quality level is equal to or above a minimum quality thresholdlevel and determines that the content quality level is unfavorable whenthe content quality level is less than the minimum quality thresholdlevel.

When the content quality level is unfavorable, the method continues atstep 222 where the processing module facilitates acquisition and furthercontent. For example, the processing module issues further contentrequests and receives further content for analysis. When the contentquality level is favorable, the method continues at step 224 where theprocessing module issues a collections response to the requester. Theissuing includes generating the collections response and sending thecollections response to the requester. The generating of the collectionsresponse may include transforming the received content into transformedcontent in accordance with a transformation approach (e.g.,reformatting, interpreting absolute meaning and translating into anotherlanguage in accordance with the absolute meaning, etc.).

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 5C is a schematic block diagram of an embodiment of the querymodule 124 of FIG. 4A that includes an answer acquisition module 230, acontent requirements module 232 a source requirements module 234, acontent security module 236, an answer timing module 238, an answertransformation module 240, and an answer quality module 242. Generally,an embodiment of this invention presents solutions where the querymodule 124 supports responding to a query.

In an example of operation of the responding to the query, the answeracquisition module 230 receives a query request 136 from a requester.The answer acquisition module 230 obtains content requirementsinformation 248 based on the query request 136. The content requirementsinformation 248 includes one or more of content parameters, a desiredcontent type, a desired content source if any, a content type if any,candidate source identifiers, a content profile, and a question of thequery request 136. For example, the answer acquisition module 230receives the content requirements information 248 from the contentrequirements module 232, where the content requirements module 232generates the content requirements information 248 based on a contentrequirements information request from the answer acquisition module 230and where the answer acquisition module 230 generates the contentrequirements information request based on the query request 136.

The answer acquisition module 230 obtains source requirementsinformation 250 based on the query request 136. The source requirementsinformation 250 includes one or more of candidate source identifiers, acontent profile, a desired source parameter, recommended sourceparameters, source priority levels, and recommended source accesstiming. For example, the answer acquisition module 230 receives thesource requirements information 250 from the source requirements module234, where the source requirements module 234 generates the sourcerequirements information 250 based on a source requirements informationrequest from the answer acquisition module 230 and where the answeracquisition module 230 generates the source requirements informationrequest based on the query request 136.

The answer acquisition module 230 obtains answer timing information 254based on the query request 136. The answer timing information 254includes one or more of requested answer timing, confirmed answertiming, source access testing results, estimated velocity of contentupdates, content freshness, timestamps, predicted time available,requested content acquisition trigger, and a content acquisition triggerdetected indicator. For example, the answer acquisition module 230receives the answer timing information 254 from the answer timing module238, where the answer timing module 238 generates the answer timinginformation 254 based on an answer timing information request from theanswer acquisition module 230 and where the answer acquisition module230 generates the answer timing information request based on the queryrequest 136.

Having obtained the content requirements information 248, the sourcerequirements information 250, and the answer timing information 254, theanswer acquisition module 230 determines whether to issue an IEI request244 and/or a collections request 132 based on one or more of the contentrequirements information 248, the source requirements information 250,and the answer timing information 254. For example, the answeracquisition module 230 selects the IEI request 244 when an immediateanswer to a simple query request 136 is required and is expected to havea favorable quality level. As another example, the answer acquisitionmodule 230 selects the collections request 132 when a longer-term answeris required as indicated by the answer timing information to beforeand/or when the query request 136 has an unfavorable quality level.

When issuing the IEI request 244, the answer acquisition module 230generates the IEI request 244 in accordance with security information252 received from the content security module 236 and based on one ormore of the content requirements information 248, the sourcerequirements information 250, and the answer timing information 254.Having generated the IEI request 244, the answer acquisition module 230sends the IEI request 244 to at least one IEI module.

When issuing the collections request 132, the answer acquisition module230 generates the collections request 132 in accordance with thesecurity information 252 received from the content security module 236and based on one or more of the content requirements information 248,the source requirements information 250, and the answer timinginformation 254. Having generated the collections request 132, theanswer acquisition module 230 sends the collections request 132 to atleast one collections module. Alternatively, the answer acquisitionmodule 230 facilitate sending of the collections request 132 to one ormore various user devices (e.g., to access a subject matter expert). Theanswer acquisition module 230 determines a quality level of a receivedanswer extracted from a collections response 134 and/or an IEI response246. For example, the answer acquisition module 230 extracts the qualitylevel of the received answer from answer quality information 258received from the answer quality module 242 in response to an answerquality request from the answer acquisition module 230. When the qualitylevel is unfavorable, the answer acquisition module 230 facilitatesobtaining a further answer. The facilitation includes issuing at leastone of a further IEI request 244 and a further collections request 132to generate a further answer for further quality testing. When thequality level is favorable, the answer acquisition module 230 issues aquery response 140 to the requester. The issuing includes generating thequery response 140 based on answer transformation information 256received from the answer transformation module 240, where the answertransformation module 240 generates the answer transformationinformation 256 to include a transformed answer based on receiving theanswer from the answer acquisition module 230. The answer transformationinformation 250 6A further include the question, a desired format of theanswer, available formats, recommended formatting, received IEIresponses, transformation instructions, and transformed IEI responsesinto an answer.

FIG. 5D is a logic diagram of an embodiment of a method for providing aresponse to a query within a computing system. In particular, a methodis presented for use in conjunction with one or more functions andfeatures described in conjunction with FIGS. 1-3, 4A-4C, 5C, and alsoFIG. 5D. The method includes step 270 where a processing module of oneor more processing modules of one or more computing devices of thecomputing system receives a query request (e.g., a question) from arequester. The method continues at step 272 where the processing moduledetermines content requirements information. The determining includesinterpreting the query request to produce the content requirements. Themethod continues at step 274 where the processing module determinessource requirements information. The determining includes interpretingthe query request to produce the source requirements. The methodcontinues at step 276 where the processing module determines answertiming information. The determining includes interpreting the queryrequest to produce the answer timing information.

The method continues at step 278 the processing module determineswhether to issue an JET request and/or a collections request. Forexample, the determining includes selecting the JET request when theanswer timing information indicates that a simple one-time answer isappropriate. As another example, the processing module selects thecollections request when the answer timing information indicates thatthe answer is associated with a series of events over an event timeframe.

When issuing the JET request, the method continues at step 280 where theprocessing module issues the JET request to an IEI module. The issuingincludes generating the IEI request in accordance with securityinformation and based on one or more of the content requirementsinformation, the source requirements information, and the answer timinginformation.

When issuing the collections request, the method continues at step 282where the processing module issues the collections request to acollections module. The issuing includes generating the collectionsrequest in accordance with the security information and based on one ormore of the content requirements information, the source requirementsinformation, and the answer timing information. Alternatively, theprocessing module issues both the IEI request and the collectionsrequest when a satisfactory partial answer may be provided based on acorresponding IEI response and a further more generalized and specificanswer may be provided based on a corresponding collections response andassociated further IEI response.

The method continues at step 284 where the processing module determinesa quality level of a received answer. The determining includesextracting the answer from the collections response and/or the IEIresponse and interpreting the answer in accordance with one or more ofthe content requirements information, the source requirementsinformation, the answer timing information, and the query request toproduce the quality level. The method branches to step 288 when thequality level is favorable and the method continues to step 286 when thequality level is unfavorable. For example, the processing moduleindicates that the quality level is favorable when the quality level isequal to or greater than a minimum answer quality threshold level. Asanother example, the processing module indicates that the quality levelis unfavorable when the quality level is less than the minimum answerquality threshold level.

When the quality level is unfavorable, the method continues at step 286where the processing module obtains a further answer. The obtainingincludes at least one of issuing a further IEI request and a furthercollections request to facilitate obtaining of a further answer forfurther answer quality level testing as the method loops back to step270. When the quality level is favorable, the method continues at step288 where the processing module issues a query response to therequester. The issuing includes transforming the answer into atransformed answer in accordance with an answer transformation approach(e.g., formatting, further interpretations of the virtual question inlight of the answer and further knowledge) and sending the transformedanswer to the requester as the query response.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 5E is a schematic block diagram of an embodiment of the identigenentigen intelligence (IEI) module 122 of FIG. 4A that includes a contentingestion module 300, an element identification module 302, andinterpretation module 304, and answer resolution module 306, and an IEIcontrol module 308. Generally, an embodiment of this invention presentssolutions where the IEI module 122 supports interpreting content toproduce knowledge that may be utilized to answer questions.

In an example of operation of the producing and utilizing of theknowledge, the content ingestion module 300 generates formatted content314 based on question content 312 and/or source content 310, where theIEI module 122 receives an IEI request 244 that includes the questioncontent 312 and the IEI module 122 receives a collections response 134that includes the source content 310. The source content 310 includescontent from a source extracted from the collections response 134. Thequestion content 312 includes content extracted from the IEI request 244(e.g., content paired with a question). The content ingestion module 300generates the formatted content 314 in accordance with a formattingapproach (e.g., creating proper sentences from words of the content).The formatted content 314 includes modified content that is compatiblewith subsequent element identification (e.g., complete sentences,combinations of words and interpreted sounds and/or inflection cues withtemporal associations of words).

The element identification module 302 processes the formatted content314 based on element rules 318 and an element list 332 to produceidentified element information 340. Rules 316 includes the element rules318 (e.g., match, partial match, language translation, etc.). Lists 330includes the element list 332 (e.g., element ID, element context ID,element usage ID, words, characters, symbols etc.). The IEI controlmodule 308 may provide the rules 316 and the lists 330 by accessingstored data 360 from a memory associated with the IEI module 122.Generally, an embodiment of this invention presents solutions where thestored data 360 may further include one or more of a descriptivedictionary, categories, representations of element sets, element list,sequence data, pending questions, pending request, recognized elements,unrecognized elements, errors, etc.

The identified element information 340 includes one or more ofidentifiers of elements identified in the formatted content 314, mayinclude ordering and/or sequencing and grouping information. Forexample, the element identification module 302 compares elements of theformatted content 314 to known elements of the element list 332 toproduce identifiers of the known elements as the identified elementinformation 340 in accordance with the element rules 318. Alternatively,the element identification module 302 outputs un-identified elementinformation 342 to the IEI control module 308, where the un-identifiedelement information 342 includes temporary identifiers for elements notidentifiable from the formatted content 314 when compared to the elementlist 332.

The interpretation module 304 processes the identified elementinformation 340 in accordance with interpretation rules 320 (e.g.,potentially valid permutations of various combinations of identifiedelements), question information 346 (e.g., a question extracted from theIEI request to hundred 44 which may be paired with content associatedwith the question), and a groupings list 334 (e.g., representations ofassociated groups of representations of things, a set of elementidentifiers, valid element usage IDs in accordance with similar, anelement context, permutations of sets of identifiers for possibleinterpretations of a sentence or other) to produce interpretedinformation 344. The interpreted information 344 includes potentiallyvalid interpretations of combinations of identified elements. Generally,an embodiment of this invention presents solutions where theinterpretation module 304 supports producing the interpreted information344 by considering permutations of the identified element information340 in accordance with the interpretation rules 320 and the groupingslist 334.

The answer resolution module 306 processes the interpreted information344 based on answer rules 322 (e.g., guidance to extract a desiredanswer), the question information 346, and inferred question information352 (e.g., posed by the IEI control module or analysis of generalcollections of content or refinement of a stated question from arequest) to produce preliminary answers 354 and an answer quality level356. The answer generally lies in the interpreted information 344 asboth new content received and knowledge based on groupings list 334generated based on previously received content. The preliminary answers354 includes an answer to a stated or inferred question that subjectfurther refinement. The answer quality level 356 includes adetermination of a quality level of the preliminary answers 354 based onthe answer rules 322. The inferred question information 352 may furtherbe associated with time information 348, where the time informationincludes one or more of current real-time, a time reference associatedwith entity submitting a request, and a time reference of a collectionsresponse.

When the JET control module 308 determines that the answer quality level356 is below an answer quality threshold level, the JET control module308 facilitates collecting of further content (e.g., by issuing acollections request 132 and receiving corresponding collectionsresponses 134 for analysis). When the answer quality level 356 comparesfavorably to the answer quality threshold level, the JET control module308 issues an JET response 246 based on the preliminary answers 354.When receiving training information 358, the JET control module 308facilitates updating of one or more of the lists 330 and the rules 316and stores the updated list 330 and the updated rules 316 in thememories as updated stored data 360.

FIG. 5F is a logic diagram of an embodiment of a method for analyzingcontent within a computing system. In particular, a method is presentedfor use in conjunction with one or more functions and features describedin conjunction with FIGS. 1-3, 4A-4C, 5E, and also FIG. 5F. The methodincludes step 370 where a processing module of one or more processingmodules of one or more computing devices of the computing systemfacilitates updating of one or more rules and lists based on one or moreof received training information and received content. For example, theprocessing module updates rules with received rules to produce updatedrules and updates element lists with received elements to produceupdated element lists. As another example, the processing moduleinterprets the received content to identify a new word for at leasttemporary inclusion in the updated element list.

The method continues at step 372 where the processing module transformsat least some of the received content into formatted content. Forexample, the processing module processes the received content inaccordance with a transformation approach to produce the formattedcontent, where the formatted content supports compatibility withsubsequent element identification (e.g., typical sentence structures ofgroups of words).

The method continues at step 374 where the processing module processesthe formatted content based on the rules and the lists to produceidentified element information and/or an identified element information.For example, the processing module compares the formatted content toelement lists to identify a match producing identifiers for identifiedelements or new identifiers for unidentified elements when there is nomatch.

The method continues at step 376 with a processing module processes theidentified element information based on rules, the lists, and questioninformation to produce interpreted information. For example, theprocessing module compares the identified element information toassociated groups of representations of things to generate potentiallyvalid interpretations of combinations of identified elements.

The method continues at step 378 where the processing module processesthe interpreted information based on the rules, the questioninformation, and inferred question information to produce preliminaryanswers. For example, the processing module matches the interpretedinformation to one or more answers (e.g., embedded knowledge based on afact base built from previously received content) with highestcorrectness likelihood levels that is subject to further refinement.

The method continues at step 380 where the processing module generatesan answer quality level based on the preliminary answers, the rules, andthe inferred question information. For example, the processing modulepredicts the answer correctness likelihood level based on the rules, theinferred question information, and the question information. The methodbranches to step 384 when the answer quality level is favorable and themethod continues to step 382 when the answer quality level isunfavorable. For example, the generating of the answer quality levelfurther includes the processing module indicating that the answerquality level is favorable when the answer quality level is greater thanor equal to a minimum answer quality threshold level. As anotherexample, the generating of the answer quality level further includes theprocessing module indicating that the answer quality level isunfavorable when the answer quality level is less than the minimumanswer quality threshold level.

When the answer quality level is unfavorable, the method continues atstep 382 where the processing module facilitates gathering clarifyinginformation. For example, the processing module issues a collectionsrequest to facilitate receiving further content and or request questionclarification from a question requester. When the answer quality levelis favorable, the method continues at step 384 where the processingmodule issues a response that includes one or more answers based on thepreliminary answers and/or further updated preliminary answers based ongathering further content. For example, the processing module generatesa response that includes one or more answers and the answer qualitylevel and issues the response to the requester.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 6A is a schematic block diagram of an embodiment of the elementidentification module 302 of FIG. 5A and the interpretation module 304of FIG. 5A. The element identification module 302 includes an elementmatching module 400 and an element grouping module 402. Theinterpretation module 304 includes a grouping matching module 404 and agrouping interpretation module 406. Generally, an embodiment of thisinvention presents solutions where the element identification module 302supports identifying potentially valid permutations of groupings ofelements while the interpretation module 304 interprets the potentiallyvalid permutations of groupings of elements to produce interpretedinformation that includes the most likely of groupings based on aquestion.

In an example of operation of the identifying of the potentially validpermutations of groupings of elements, when matching elements of theformatted content 314, the element matching module 400 generates matchedelements 412 (e.g., identifiers of elements contained in the formattedcontent 314) based on the element list 332. For example, the elementmatching module 400 matches a received element to an element of theelement list 332 and outputs the matched elements 412 to include anidentifier of the matched element. When finding elements that areunidentified, the element matching module 400 outputs un-recognizedwords information 408 (e.g., words not in the element list 332, maytemporarily add) as part of un-identified element information 342. Forexample, the element matching module 400 indicates that a match cannotbe made between a received element of the formatted content 314,generates the unrecognized words info 408 to include the receivedelement and/or a temporary identifier, and issues and updated elementlist 414 that includes the temporary identifier and the correspondingunidentified received element.

The element grouping module 402 analyzes the matched elements 412 inaccordance with element rules 318 to produce grouping error information410 (e.g., incorrect sentence structure indicators) when a structuralerror is detected. The element grouping module 402 produces identifiedelement information 340 when favorable structure is associated with thematched elements in accordance with the element rules 318. Theidentified element information 340 may further include groupinginformation of the plurality of permutations of groups of elements(e.g., several possible interpretations), where the grouping informationincludes one or more groups of words forming an associated set and/orsuper-group set of two or more subsets when subsets share a common coreelement.

In an example of operation of the interpreting of the potentially validpermutations of groupings of elements to produce the interpretedinformation, the grouping matching module 404 analyzes the identifiedelement information 340 in accordance with a groupings list 334 toproduce validated groupings information 416. For example, the groupingmatching module 404 compares a grouping aspect of the identified elementinformation 340 (e.g., for each permutation of groups of elements ofpossible interpretations), generates the validated groupings information416 to include identification of valid permutations aligned with thegroupings list 334. Alternatively, or in addition to, the groupingmatching module 404 generates an updated groupings list 418 whendetermining a new valid grouping (e.g., has favorable structure andinterpreted meaning) that is to be added to the groupings list 334.

The grouping interpretation module 406 interprets the validatedgroupings information 416 based on the question information 346 and inaccordance with the interpretation rules 320 to produce interpretedinformation 344 (e.g., most likely interpretations, next most likelyinterpretations, etc.). For example, the grouping interpretation module406 obtains context, obtains favorable historical interpretations,processes the validated groupings based on interpretation rules 320,where each interpretation is associated with a correctness likelihoodlevel.

FIG. 6B is a logic diagram of an embodiment of a method for interpretinginformation within a computing system. In particular, a method ispresented for use in conjunction with one or more functions and featuresdescribed in conjunction with FIGS. 1-3, 4A-4C, 5E-5F, 6A, and also FIG.6B. The method includes step 430 where a processing module of one ormore processing modules of one or more computing devices of thecomputing system analyzes formatted content. For example, the processingmodule attempt to match a received element of the formatted content toone or more elements of an elements list. When there is no match, themethod branches to step 434 and when there is a match, the methodcontinues to step 432. When there is a match, the method continues atstep 432 where the processing module outputs matched elements (e.g., toinclude the matched element and/or an identifier of the matchedelement). When there is no match, the method continues at step 434 wherethe processing module outputs unrecognized words (e.g., elements and/ora temporary identifier for the unmatched element).

The method continues at step 436 where the processing module analyzesmatched elements. For example, the processing module attempt to match adetected structure of the matched elements (e.g., chained elements as ina received sequence) to favorable structures in accordance with elementrules. The method branches to step 440 when the analysis is unfavorableand the method continues to step 438 when the analysis is favorable.When the analysis is favorable matching a detected structure to thefavorable structure of the element rules, the method continues at step438 where the processing module outputs identified element information(e.g., an identifier of the favorable structure, identifiers of each ofthe detected elements). When the analysis is unfavorable matching adetected structure to the favorable structure of the element rules, themethod continues at step 440 where the processing module outputsgrouping error information (e.g., a representation of the incorrectstructure, identifiers of the elements of the incorrect structure, atemporary new identifier of the incorrect structure).

The method continues at step 442 where the processing module analyzesthe identified element information to produce validated groupingsinformation. For example, the processing module compares a groupingaspect of the identified element information and generates the validatedgroupings information to include identification of valid permutationsthat align with the groupings list. Alternatively, or in addition to,the processing module generates an updated groupings list whendetermining a new valid grouping.

The method continues at step 444 where the processing module interpretsthe validated groupings information to produce interpreted information.For example, the processing module obtains one or more of context andhistorical interpretations and processes the validated groupings basedon interpretation rules to generate the interpreted information, whereeach interpretation is associated with a correctness likelihood level(e.g., a quality level).

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 6C is a schematic block diagram of an embodiment of the answerresolution module 306 of FIG. 5A that includes an interim answer module460, and answer prioritization module 462, and a preliminary answerquality module 464. Generally, an embodiment of this invention presentssolutions where the answer resolution module 306 supports producing ananswer for interpreted information 344.

In an example of operation of the providing of the answer, the interimanswer module 460 analyzes the interpreted information 344 based onquestion information 346 and inferred question information 352 toproduce interim answers 466 (e.g., answers to stated and/or inferredquestions without regard to rules that is subject to furtherrefinement). The answer prioritization module 462 analyzes the interimanswers 466 based on answer rules 322 to produce preliminary answer 354.For example, the answer prioritization module 462 identifies allpossible answers from the interim answers 466 that conform to the answerrules 322.

The preliminary answer quality module 464 analyzes the preliminaryanswers 354 in accordance with the question information 346, theinferred question information 352, and the answer rules 322 to producean answer quality level 356. For example, for each of the preliminaryanswers 354, the preliminary answer quality module 464 may compare a fitof the preliminary answer 354 to a corresponding previous answer andquestion quality level, calculate the answer quality level 356 based ona level of conformance to the answer rules 322, calculate the answerquality level 356 based on alignment with the inferred questioninformation 352, and determine the answer quality level 356 based on aninterpreted correlation with the question information 346.

FIG. 6D is a logic diagram of an embodiment of a method for producing ananswer within a computing system. In particular, a method is presentedfor use in conjunction with one or more functions and features describedin conjunction with FIGS. 1-3, 4A-4C, 5E-5F, 6C, and also FIG. 6D. Themethod includes step 480 where a processing module of one or moreprocessing modules of one or more computing devices of the computingsystem analyzes received interpreted information based on questioninformation and inferred question information to produce one or moreinterim answers. For example, the processing module generates potentialanswers based on patterns consistent with previously produced knowledgeand likelihood of correctness.

The method continues at step 482 where the processing module analyzesthe one or more interim answers based on answer rules to producepreliminary answers. For example, the processing module identifies allpossible answers from the interim answers that conform to the answerrules. The method continues at step 484 where the processing moduleanalyzes the preliminary answers in accordance with the questioninformation, the inferred question information, and the answer rules toproduce an answer quality level. For example, for each of the elementaryanswers, the processing module may compare a fit of the preliminaryanswer to a corresponding previous answer-and-answer quality level,calculate the answer quality level based on performance to the answerrules, calculate answer quality level based on alignment with theinferred question information, and determine the answer quality levelbased on interpreted correlation with the question information.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 7A is an information flow diagram for interpreting informationwithin a computing system, where sets of entigens 504 are interpretedfrom sets of identigens 502 which are interpreted from sentences ofwords 500. Such identigen entigen intelligence (IP processing of thewords (e.g., to JET process) includes producing one or more of interimknowledge, a preliminary answer, and an answer quality level. Forexample, the JET processing includes identifying permutations ofidentigens of a phrase of a sentence (e.g., interpreting humanexpressions to produce identigen groupings for each word of ingestedcontent), reducing the permutations of identigens (e.g., utilizing rulesto eliminate unfavorable permutations), mapping the reduced permutationsof identigens to at least one set of entigens (e.g., most likelyidentigens become the entigens) to produce the interim knowledge,processing the knowledge in accordance with a knowledge base (e.g.,comparing the set of entigens to the knowledge base) to produce apreliminary answer, and generating the answer quality level based on thepreliminary answer for a corresponding domain.

Human expressions are utilized to portray facts and fiction about thereal world. The real-world includes items, actions, and attributes. Thehuman expressions include textual words, textual symbols, images, andother sensorial information (e.g., sounds). It is known that many words,within a given language, can mean different things based on groupingsand orderings of the words. For example, the sentences of words 500 caninclude many different forms of sentences that mean vastly differentthings even when the words are very similar.

The present invention presents solutions where the computing system 10supports producing a computer-based representation of a truest meaningpossible of the human expressions given the way that multitudes of humanexpressions relate to these meanings. As a first step of the flowdiagram to transition from human representations of things to a mostprecise computer representation of the things, the computer identifiesthe words, phrases, sentences, etc. from the human expressions toproduce the sets of identigens 502. Each identigen includes anidentifier of their meaning and an identifier of an instance for eachpossible language, culture, etc. For example, the words car andautomobile share a common meaning identifier but have different instanceidentifiers since they are different words and are spelled differently.As another example, the word duck is associated both with a bird and anaction to elude even though they are spelled the same. In this examplethe bird duck has a different meaning than the elude duck and as sucheach has a different meaning identifier of the corresponding identigens.

As a second step of the flow diagram to transition from humanrepresentations of things to the most precise computer representation ofthe things, the computer extracts meaning from groupings of theidentified words, phrases, sentences, etc. to produce the sets ofentigens 504. Each entigen includes an identifier of a singleconceivable and perceivable thing in space and time (e.g., independentof language and other aspects of the human expressions). For example,the words car and automobile are different instances of the same meaningand point to a common shared entigen. As another example, the word duckfor the bird meaning has an associated unique entigen that is differentthan the entigen for the word duck for the elude meaning.

As a third step of the flow diagram to transition from human expressionsof things to the most precise computer representation of the things, thecomputer reasons facts from the extracted meanings. For example, thecomputer maintains a fact-based of the valid meanings from the validgroupings or sets of entigens so as to support subsequent inferences,deductions, rationalizations of posed questions to produce answers thatare aligned with a most factual view. As time goes on, and as an entigenhas been identified, it can encounter an experience transformations intime, space, attributes, actions, and words which are used to identifyit without creating contradictions or ever losing its identity.

FIG. 7B is a relationship block diagram illustrating an embodiment ofrelationships between things 510 and representations of things 512within a computing system. The things 510 includes conceivable andperceivable things including actions 522, items 524, and attributes 526.The representation of things 512 includes representations of things usedby humans 514 and representation of things used by of computing devices516 of embodiments of the present invention. The things 510 relates tothe representations of things used by humans 514 where the inventionpresents solutions where the computing system 10 supports mapping therepresentations of things used by humans 514 to the representations ofthings used by computing devices 516, where the representations ofthings used by computing devices 516 map back to the things 510.

The representations of things used by humans 514 includes textual words528, textual symbols 530, images (e.g., non-textual) 532, and othersensorial information 534 (e.g., sounds, sensor data, electrical fields,voice inflections, emotion representations, facial expressions,whistles, etc.). The representations of things used by computing devices516 includes identigens 518 and entigens 520. The representations ofthings used by humans 514 maps to the identigens 518 and the identigens518 map to the entigens 520. The entigens 520 uniquely maps back to thethings 510 in space and time, a truest meaning the computer is lookingfor to create knowledge and answer questions based on the knowledge.

To accommodate the mapping of the representations of things used byhumans 514 to the identigens 518, the identigens 518 is partitioned intoactenyms 544 (e.g., actions), itenyms 546 (e.g., items), attrenyms 548(e.g., attributes), and functionals 550 (e.g., that join and/ordescribe). Each of the actenyms 544, itenyms 546, and attrenyms 548 maybe further classified into singulatums 552 (e.g., identify one uniqueentigen) and pluratums 554 (e.g., identify a plurality of entigens thathave similarities).

Each identigen 518 is associated with an identigens identifier (IDN)536. The IDN 536 includes a meaning identifier (ID) 538 portion, aninstance ID 540 portion, and a type ID 542 portion. The meaning ID 538includes an identifier of common meaning. The instance ID 540 includesan identifier of a particular word and language. The type ID 542includes one or more identifiers for actenyms, itenyms, attrenyms,singulatums, pluratums, a time reference, and any other reference todescribe the IDN 536. The mapping of the representations of things usedby humans 514 to the identigens 518 by the computing system of thepresent invention includes determining the identigens 518 in accordancewith logic and instructions for forming groupings of words.

Generally, an embodiment of this invention presents solutions where theidentigens 518 map to the entigens 520. Multiple identigens may map to acommon unique entigen. The mapping of the identigens 518 to the entigens520 by the computing system of the present invention includesdetermining entigens in accordance with logic and instructions forforming groupings of identigens.

FIG. 7C is a diagram of an embodiment of a synonym words table 570within a computing system, where the synonym words table 570 includesmultiple fields including textual words 572, identigens 518, andentigens 520. The identigens 518 includes fields for the meaningidentifier (ID) 538 and the instance ID 540. The computing system of thepresent invention may utilize the synonym words table 570 to map textualwords 572 to identigens 518 and map the identigens 518 to entigens 520.For example, the words car, automobile, auto, bil (Swedish), carro(Spanish), and bil (Danish) all share a common meaning but are differentinstances (e.g., different words and languages). The words map to acommon meaning ID but to individual unique instant identifiers. Each ofthe different identigens map to a common entigen since they describe thesame thing.

FIG. 7D is a diagram of an embodiment of a polysemous words table 576within a computing system, where the polysemous words table 576 includesmultiple fields including textual words 572, identigens 518, andentigens 520. The identigens 518 includes fields for the meaningidentifier (ID) 538 and the instance ID 540. The computing system of thepresent invention may utilize the polysemous words table 576 to maptextual words 572 to identigens 518 and map the identigens 518 toentigens 520. For example, the word duck maps to four differentidentigens since the word duck has four associated different meanings(e.g., bird, fabric, to submerge, to elude) and instances. Each of theidentigens represent different things and hence map to four differententigens.

FIG. 7E is a diagram of an embodiment of transforming words intogroupings within a computing system that includes a words table 580, agroupings of words section to validate permutations of groupings, and agroupings table 584 to capture the valid groupings. The words table 580includes multiple fields including textual words 572, identigens 518,and entigens 520. The identigens 518 includes fields for the meaningidentifier (ID) 538, the instance ID 540, and the type ID 542. Thecomputing system of the present invention may utilize the words table580 to map textual words 572 to identigens 518 and map the identigens518 to entigens 520. For example, the word pilot may refer to a flyerand the action to fly. Each meaning has a different identigen anddifferent entigen.

The computing system the present invention may apply rules to the fieldsof the words table 580 to validate various groupings of words. Thosethat are invalid are denoted with a “X” while those that are valid areassociated with a check mark. For example, the grouping “pilot Tom” isinvalid when the word pilot refers to flying and Tom refers to a person.The identigen combinations for the flying pilot and the person Tom aredenoted as invalid by the rules. As another example, the grouping “pilotTom” is valid when the word pilot refers to a flyer and Tom refers tothe person. The identigen combinations for the flyer pilot and theperson Tom are denoted as valid by the rules.

The groupings table 584 includes multiple fields including grouping ID586, word strings 588, identigens 518, and entigens 520. The computingsystem of the present invention may produce the groupings table 584 as astored fact base for valid and/or invalid groupings of words identifiedby their corresponding identigens. For example, the valid grouping“pilot Tom” referring to flyer Tom the person is represented with agrouping identifier of 3001 and identity and identifiers 150.001 and457.001. The entigen field 520 may indicate associated entigens thatcorrespond to the identigens. For example, entigen e717 corresponds tothe flyer pilot meaning and entigen e61 corresponds to the time theperson meaning. Alternatively, or in addition to, the entigen field 520may be populated with a single entigen identifier (EM).

The word strings field 588 may include any number of words in a string.Different ordering of the same words can produce multiple differentstrings and even different meanings and hence entigens. More broadly,each entry (e.g., role) of the groupings table 584 may refer togroupings of words, two or more word strings, an idiom, just identigens,just entigens, and/or any combination of the preceding elements. Eachentry has a unique grouping identifier. An idiom may have a uniquegrouping ID and include identifiers of original word identigens andreplacing identigens associated with the meaning of the idiom not justthe meaning of the original words. Valid groupings may still haveambiguity on their own and may need more strings and/or context toselect a best fit when interpreting a truest meaning of the grouping.

FIG. 8A is a data flow diagram for accumulating knowledge within acomputing system, where a computing device, at a time=t0, ingests andprocesses facts 598 at a step 590 based on rules 316 and fact baseinformation 600 to produce groupings 602 for storage in a fact base 592(e.g., words, phrases, word groupings, identigens, entigens, qualitylevels). The facts 598 may include information from books, archive data,Central intelligence agency (CIA) world fact book, trusted content, etc.The ingesting may include filtering to organize and promote better validgroupings detection (e.g., considering similar domains together). Thegroupings 602 includes one or more of groupings identifiers, identigenidentifiers, entigen identifiers, and estimated fit quality levels. Theprocessing step 590 may include identifying identigens from words of thefacts 598 in accordance with the rules 316 and the fact base info 600and identifying groupings utilizing identigens in accordance with rules316 and fact base info 600.

Subsequent to ingestion and processing of the facts 598 to establish thefact base 592, at a time=t1+, the computing device ingests and processesnew content 604 at a step 594 in accordance with the rules 316 and thefact base information 600 to produce preliminary grouping 606. The newcontent may include updated content (e.g., timewise) from periodicals,newsfeeds, social media, etc. The preliminary grouping 606 includes oneor more of preliminary groupings identifiers, preliminary identigenidentifiers, preliminary entigen identifiers, estimated fit qualitylevels, and representations of unidentified words.

The computing device validates the preliminary groupings 606 at a step596 based on the rules 316 and the fact base info 600 to produce updatedfact base info 608 for storage in the fact base 592. The validatingincludes one or more of reasoning a fit of existing fact base info 600with the new preliminary grouping 606, discarding preliminary groupings,updating just time frame information associated with an entry of theexisting fact base info 600 (e.g., to validate knowledge for thepresent), creating new entigens, and creating a median entigen tosummarize portions of knowledge within a median indicator as a qualitylevel indicator (e.g., suggestive not certain).

Storage of the updated fact base information 608 captures patterns thatdevelop by themselves instead of searching for patterns as in prior artartificial intelligence systems. Growth of the fact base 592 enablessubsequent reasoning to create new knowledge including deduction,induction, inference, and inferential sentiment (e.g., a chain ofsentiment sentences). Examples of sentiments includes emotion, beliefs,convictions, feelings, judgments, notions, opinions, and views.

FIG. 8B is a diagram of an embodiment of a groupings table 620 within acomputing system. The groupings table 620 includes multiple fieldsincluding grouping ID 586, word strings 588, an IF string 622 and a THENstring 624. Each of the fields for the IF string 622 and the THEN string624 includes fields for an identigen (IDN) string 626, and an entigen(EM) string 628. The computing system of the present invention mayproduce the groupings table 620 as a stored fact base to enable IF THENbased inference to generate a new knowledge inference 630.

As a specific example, grouping 5493 points out the logic of IF someonehas a tumor, THEN someone is sick and the grouping 5494 points of thelogic that IF someone is sick, THEN someone is sad. As a result ofutilizing inference, the new knowledge inference 630 may producegrouping 5495 where IF someone has a tumor, THEN someone is possibly sad(e.g., or is sad).

FIG. 8C is a data flow diagram for answering questions utilizingaccumulated knowledge within a computing system, where a computingdevice ingests and processes question information 346 at a step 640based on rules 316 and fact base info 600 from a fact base 592 toproduce preliminary grouping 606. The ingesting and processing questionsstep 640 includes identifying identigens from words of a question inaccordance with the rules 316 and the fact base information 600 and mayalso include identifying groupings from the identified identigens inaccordance with the rules 316 and the fact base information 600.

The computing device validates the preliminary grouping 606 at a step596 based on the rules 316 and the fact base information 600 to produceidentified element information 340. For example, the computing devicereasons fit of existing fact base information with new preliminarygroupings 606 to produce the identified element information 340associated with highest quality levels. The computing device interpretsa question of the identified element information 340 at a step 642 basedon the rules 316 and the fact base information 600. The interpreting ofthe question may include separating new content from the question andreducing the question based on the fact base information 600 and the newcontent.

The computing device produces preliminary answers 354 from theinterpreted information 344 at a resolve answer step 644 based on therules 316 and the fact base information 600. For example, the computingdevice compares the interpreted information 344 two the fact baseinformation 600 to produce the preliminary answers 354 with highestquality levels utilizing one or more of deduction, induction,inferencing, and applying inferential sentiments logic. Alternatively,or in addition to, the computing device may save new knowledgeidentified from the question information 346 to update the fact base592.

FIG. 8D is a data flow diagram for answering questions utilizinginterference within a computing system that includes a groupings table648 and the resolve answer step 644 of FIG. 8C. The groupings table 648includes multiple fields including fields for a grouping (GRP)identifier (ID) 586, word strings 588, an identigen (IDN) string 626,and an entigen (EM) 628. The groupings table 648 may be utilized tobuild a fact base to enable resolving a future question into an answer.For example, the grouping 8356 notes knowledge that Michael sleeps eighthours and grouping 8357 notes that Michael usually starts to sleep at 11PM.

In a first question example that includes a question “Michaelsleeping?”, the resolve answer step 644 analyzes the question from theinterpreted information 344 in accordance with the fact base information600, the rules 316, and a real-time indicator that the current time is 1AM to produce a preliminary answer of “possibly YES” when inferring thatMichael is probably sleeping at 1 AM when Michael usually startssleeping at 11 PM and Michael usually sleeps for a duration of eighthours.

In a second question example that includes the question “Michaelsleeping?”, the resolve answer step 644 analyzes the question from theinterpreted information 344 in accordance with the fact base information600, the rules 316, and a real-time indicator that the current time isnow 11 AM to produce a preliminary answer of “possibly NO” wheninferring that Michael is probably not sleeping at 11 AM when Michaelusually starts sleeping at 11 PM and Michael usually sleeps for aduration of eight hours.

FIG. 8E is a relationship block diagram illustrating another embodimentof relationships between things and representations of things within acomputing system. While things in the real world are described withwords, it is often the case that a particular word has multiple meaningsin isolation. Interpreting the meaning of the particular word may hingeon analyzing how the word is utilized in a phrase, a sentence, multiplesentences, paragraphs, and even whole documents or more. Describing andstratifying the use of words, word types, and possible meanings help ininterpreting a true meaning.

Humans utilize textual words 528 to represent things in the real world.Quite often a particular word has multiple instances of differentgrammatical use when part of a phrase of one or more sentences. Thegrammatical use 649 of words includes the nouns and the verbs, and alsoincludes adverbs, adjectives, pronouns, conjunctions, prepositions,determiners, exclamations, etc.

As an example of multiple grammatical use, the word “bat” in the Englishlanguage can be utilized as a noun or a verb. For instance, whenutilized as a noun, the word “bat” may apply to a baseball bat or mayapply to a flying “bat.” As another instance, when utilized as a verb,the word “bat” may apply to the action of hitting or batting an object,i.e., “bat the ball.”

To stratify word types by use, the words are associated with a word type(e.g., type identifier 542). The word types include objects (e.g., items524), characteristics (e.g., attributes 526), actions 522, and thefunctionals 550 for joining other words and describing words. Forexample, when the word “bat” is utilized as a noun, the word isdescribing the object of either the baseball bat or the flying bat. Asanother example, when the word “bat” is utilized as a verb, the word isdescribing the action of hitting.

To determine possible meanings, the words, by word type, are mapped toassociative meanings (e.g., identigens 518). For each possibleassociative meaning, the word type is documented with the meaning andfurther with an identifier (ID) of the instance (e.g., an identigenidentifier).

For the example of the word “bat” when utilized as a noun for thebaseball bat, a first identigen identifier 536-1 includes a type ID542-1 associated with the object 524, an instance ID 540-1 associatedwith the first identigen identifier (e.g., unique for the baseball bat),and a meaning ID 538-1 associated with the baseball bat. For the exampleof the word “bat” when utilized as a noun for the flying bat, a secondidentigen identifier 536-2 includes a type ID 542-1 associated with theobject 524, an instance ID 540-2 associated with the second identigenidentifier (e.g., unique for the flying bat), and a meaning ID 538-2associated with the flying bat. For the example of the word “bat” whenutilized as a verb for the bat that hits, a third identigen identifier536-2 includes a type ID 542-2 associated with the actions 522, aninstance ID 540-3 associated with the third identigen identifier (e.g.,unique for the bat that hits), and a meaning ID 538-3 associated withthe bat that hits.

With the word described by a type and possible associative meanings, acombination of full grammatical use of the word within the phrase etc.,application of rules, and utilization of an ever-growing knowledge basethat represents knowledge by linked entigens, the absolute meaning(e.g., entigen 520) of the word is represented as a unique entigen. Forexample, a first entigen e1 represents the absolute meaning of abaseball bat (e.g., a generic baseball bat not a particular baseball batthat belongs to anyone), a second entigen e2 represents the absolutemeaning of the flying bat (e.g., a generic flying bat not a particularflying bat), and a third entigen e3 represents the absolute meaning ofthe verb bat (e.g., to hit).

An embodiment of methods to ingest text to produce absolute meanings forstorage in a knowledge base are discussed in greater detail withreference to FIGS. 8F-H. Those embodiments further discuss thediscerning of the grammatical use, the use of the rules, and theutilization of the knowledge base to definitively interpret the absolutemeaning of a string of words.

Another embodiment of methods to respond to a query to produce an answerbased on knowledge stored in the knowledge base are discussed in greaterdetail with reference to FIGS. 8J-L. Those embodiments further discussthe discerning of the grammatical use, the use of the rules, and theutilization of the knowledge base to interpret the query. The queryinterpretation is utilized to extract the answer from the knowledge baseto facilitate forming the query response.

FIGS. 8F and 8G are schematic block diagrams of another embodiment of acomputing system that includes the content ingestion module 300 of FIG.5E, the element identification module 302 of FIG. 5E, the interpretationmodule 304 of FIG. 5E, the IEI control module 308 of FIG. 5E, and the SSmemory 96 of FIG. 2. Generally, an embodiment of this invention providespresents solutions where the computing system 10 supports processingcontent to produce knowledge for storage in a knowledge base.

The processing of the content to produce the knowledge includes a seriesof steps. For example, a first step includes identifying words of aningested phrase to produce tokenized words. As depicted in FIG. 8F, aspecific example of the first step includes the content ingestion module300 comparing words of source content 310 to dictionary entries toproduce formatted content 314 that includes identifiers of known words.Alternatively, when a comparison is unfavorable, the temporaryidentifier may be assigned to an unknown word. For instance, the contentingestion module 300 produces identifiers associated with the words“the”, “black”, “bat”, “eats”, and “fruit” when the ingested phraseincludes “The black bat eats fruit”, and generates the formatted content314 to include the identifiers of the words.

A second step of the processing of the content to produce the knowledgeincludes, for each tokenized word, identifying one or more identigensthat correspond the tokenized word, where each identigen describes oneof an object, a characteristic, and an action. As depicted in FIG. 8F, aspecific example of the second step includes the element identificationmodule 302 performing a look up of identigen identifiers, utilizing anelement list 332 and in accordance with element rules 318, of the one ormore identigens associated with each tokenized word of the formattedcontent 314 to produce identified element information 340.

A unique identifier is associated with each of the potential object, thecharacteristic, and the action (OCA) associated with the tokenized word(e.g. sequential identigens). For instance, the element identificationmodule 302 identifies a functional symbol for “the”, identifies a singleidentigen for “black”, identifies two identigens for “bat” (e.g.,baseball bat and flying bat), identifies a single identigen for “eats”,and identifies a single identigen for “fruit.” When at least onetokenized word is associated with multiple identigens, two or morepermutations of sequential combinations of identigens for each tokenizedword result. For example, when “bat” is associated with two identigens,two permutations of sequential combinations of identigens result for theingested phrase.

A third step of the processing of the content to produce the knowledgeincludes, for each permutation of sequential combinations of identigens,generating a corresponding equation package (i.e., candidateinterpretation), where the equation package includes a sequentiallinking of pairs of identigens (e.g., relationships), where eachsequential linking pairs a preceding identigen to a next identigen, andwhere an equation element describes a relationship between pairedidentigens (OCAs) such as describes, acts on, is a, belongs to, did, didto, etc. Multiple OCAs occur for a common word when the word hasmultiple potential meanings (e.g., a baseball bat, a flying bat).

As depicted in FIG. 8F, a specific example of the third step includesthe interpretation module 304, for each permutation of identigens ofeach tokenized word of the identified element information 340, theinterpretation module 304 generates, in accordance with interpretationrules 320 and a groupings list 334, an equation package to include oneor more of the identifiers of the tokenized words, a list of identifiersof the identigens of the equation package, a list of pairing identifiersfor sequential pairs of identigens, and a quality metric associated witheach sequential pair of identigens (e.g., likelihood of a properinterpretation). For instance, the interpretation module 304 produces afirst equation package that includes a first identigen pairing of ablack bat (e.g., flying bat with a higher quality metric level), thesecond pairing of bat eats (e.g., the flying bat eats, with a higherquality metric level), and a third pairing of eats fruit, and theinterpretation module 304 produces a second equation package thatincludes a first pairing of a black bat (e.g., baseball bat, with aneutral quality metric level), the second pairing of bat eats (e.g., thebaseball bat eats, with a lower quality metric level), and a thirdpairing of eats fruit.

A fourth step of the processing of the content to produce the knowledgeincludes selecting a surviving equation package associated with a mostfavorable confidence level. As depicted in FIG. 8F, a specific exampleof the fourth step includes the interpretation module 304 applyinginterpretation rules 320 (i.e., inference, pragmatic engine, utilizingthe identifiers of the identigens to match against known validcombinations of identifiers of entigens) to reduce a number ofpermutations of the sequential combinations of identigens to produceinterpreted information 344 that includes identification of at least oneequation package as a surviving interpretation SI (e.g., higher qualitymetric level).

Non-surviving equation packages are eliminated that compare unfavorablyto pairing rules and/or are associated with an unfavorable qualitymetric levels to produce a non-surviving interpretation NSI 2 (e.g.,lower quality metric level), where an overall quality metric level maybe assigned to each equation package based on quality metric levels ofeach pairing, such that a higher quality metric level of an equationpackage indicates a higher probability of a most favorableinterpretation. For instance, the interpretation module 304 eliminatesthe equation package that includes the second pairing indicating thatthe “baseball bat eats” which is inconsistent with a desired qualitymetric level of one or more of the groupings list 334 and theinterpretation rules 320 and selects the equation package associatedwith the “flying bat eats” which is favorably consistent with the one ormore of the quality metric levels of the groupings list 334 and theinterpretation rules 320.

A fifth step of the processing of the content to produce the knowledgeutilizing the confidence level includes integrating knowledge of thesurviving equation package into a knowledge base. For example,integrating at least a portion of the reduced OCA combinations into agraphical database to produce updated knowledge. As another example, theportion of the reduced OCA combinations may be translated into rows andcolumns entries when utilizing a rows and columns database rather than agraphical database. When utilizing the rows and columns approach for theknowledge base, subsequent access to the knowledge base may utilizestructured query language (SQL) queries.

As depicted in FIG. 8G, a specific example of the fifth step includesthe JET control module 308 recovering fact base information 600 from SSmemory 96 to identify a portion of the knowledge base for potentialmodification utilizing the OCAs of the surviving interpretation SI 1(i.e., compare a pattern of relationships between the OCAs of thesurviving interpretation SI 1 from the interpreted information 344 torelationships of OCAs of the portion of the knowledge base includingpotentially new quality metric levels).

The fifth step further includes determining modifications (e.g.,additions, subtractions, further clarifications required wheninformation is complex, etc.) to the portion of the knowledge base basedon the new quality metric levels. For instance, the JET control module308 causes adding the element “black” as a “describes” relationship ofan existing bat OCA and adding the element “fruit” as an eats “does to”relationship to implement the modifications to the portion of the factbase information 600 to produce updated fact base information 608 forstorage in the SS memory 96.

FIG. 8H is a logic diagram of an embodiment of a method for processingcontent to produce knowledge for storage within a computing system. Inparticular, a method is presented for use in conjunction with one ormore functions and features described in conjunction with FIGS. 1-8E,8F, and also FIG. 8G. The method includes step 650 where a processingmodule of one or more processing modules of one or more computingdevices of the computing system identifies words of an ingested phraseto produce tokenized words. The identified includes comparing words toknown words of dictionary entries to produce identifiers of known words.

For each tokenized word, the method continues at step 651 where theprocessing module identifies one or more identigens that corresponds tothe tokenized word, where each identigen describes one of an object, acharacteristic, and an action (e.g., OCA). The identifying includesperforming a lookup of identifiers of the one or more identigensassociated with each tokenized word, where he different identifiersassociated with each of the potential object, the characteristic, andthe action associated with the tokenized word.

The method continues at step 652 where the processing module, for eachpermutation of sequential combinations of identigens, generates aplurality of equation elements to form a corresponding equation package,where each equation element describes a relationship betweensequentially linked pairs of identigens, where each sequential linkingpairs a preceding identigen to a next identigen. For example, for eachpermutation of identigens of each tokenized word, the processing modulegenerates the equation package to include a plurality of equationelements, where each equation element describes the relationship (e.g.,describes, acts on, is a, belongs to, did, did too, etc.) betweensequentially adjacent identigens of a plurality of sequentialcombinations of identigens. Each equation element may be furtherassociated with a quality metric to evaluate a favorability level of aninterpretation in light of the sequence of identigens of the equationpackage.

The method continues at step 653 where the processing module selects asurviving equation package associated with most favorableinterpretation. For example, the processing module applies rules (i.e.,inference, pragmatic engine, utilizing the identifiers of the identigensto match against known valid combinations of identifiers of entigens),to reduce the number of permutations of the sequential combinations ofidentigens to identify at least one equation package, wherenon-surviving equation packages are eliminated the compare unfavorablyto pairing rules and/or are associated with an unfavorable qualitymetric levels to produce a non-surviving interpretation, where anoverall quality metric level is assigned to each equation package basedon quality metric levels of each pairing, such that a higher qualitymetric level indicates an equation package with a higher probability offavorability of correctness.

The method continues at step 654 where the processing module integratesknowledge of the surviving equation package into a knowledge base. Forexample, the processing module integrates at least a portion of thereduced OCA combinations into a graphical database to produce updatedknowledge. The integrating may include recovering fact base informationfrom storage of the knowledge base to identify a portion of theknowledge base for potential modifications utilizing the OCAs of thesurviving equation package (i.e., compare a pattern of relationshipsbetween the OCAs of the surviving equation package to relationships ofthe OCAs of the portion of the knowledge base including potentially newquality metric levels). The integrating further includes determiningmodifications (e.g., additions, subtractions, further clarificationsrequired when complex information is presented, etc.) to produce theupdated knowledge base that is based on fit of acceptable quality metriclevels, and implementing the modifications to the portion of the factbase information to produce the updated fact base information forstorage in the portion of the knowledge base.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIGS. 8J and 8K are schematic block diagrams of another embodiment of acomputing system that includes the content ingestion module 300 of FIG.5E, the element identification module 302 of FIG. 5E, the interpretationmodule 304 of FIG. 5E, the answer resolution module 306 of FIG. 5E, andthe SS memory 96 of FIG. 2. Generally, an embodiment of this inventionprovides solutions where the computing system 10 supports for generatinga query response to a query utilizing a knowledge base.

The generating of the query response to the query includes a series ofsteps. For example, a first step includes identifying words of aningested query to produce tokenized words. As depicted in FIG. 8J, aspecific example of the first step includes the content ingestion module300 comparing words of query info 138 to dictionary entries to produceformatted content 314 that includes identifiers of known words. Forinstance, the content ingestion module 300 produces identifiers for eachword of the query “what black animal flies and eats fruit and insects?”

A second step of the generating of the query response to the queryincludes, for each tokenized word, identifying one or more identigensthat correspond the tokenized word, where each identigen describes oneof an object, a characteristic, and an action (OCA). As depicted in FIG.8J, a specific example of the second step includes the elementidentification module 302 performing a look up of identifiers, utilizingan element list 332 and in accordance with element rules 318, of the oneor more identigens associated with each tokenized word of the formattedcontent 314 to produce identified element information 340. A uniqueidentifier is associated with each of the potential object, thecharacteristic, and the action associated with a particular tokenizedword. For instance, the element identification module 302 produces asingle identigen identifier for each of the black color, an animal,flies, eats, fruit, and insects.

A third step of the generating of the query response to the queryincludes, for each permutation of sequential combinations of identigens,generating a corresponding equation package (i.e., candidateinterpretation). The equation package includes a sequential linking ofpairs of identigens, where each sequential linking pairs a precedingidentigen to a next identigen. An equation element describes arelationship between paired identigens (OCAs) such as describes, actson, is a, belongs to, did, did to, etc.

As depicted in FIG. 8J, a specific example of the third step includesthe interpretation module 304, for each permutation of identigens ofeach tokenized word of the identified element information 340,generating the equation packages in accordance with interpretation rules320 and a groupings list 334 to produce a series of equation elementsthat include pairings of identigens. For instance, the interpretationmodule 304 generates a first pairing to describe a black animal, asecond pairing to describe an animal that flies, a third pairing todescribe flies and eats, a fourth pairing to describe eats fruit, and afifth pairing to describe eats fruit and insects.

A fourth step of the generating the query response to the query includesselecting a surviving equation package associated with a most favorableinterpretation. As depicted in FIG. 8J, a specific example of the fourthstep includes the interpretation module 304 applying the interpretationrules 320 (i.e., inference, pragmatic engine, utilizing the identifiersof the identigens to match against known valid combinations ofidentifiers of entigens) to reduce the number of permutations of thesequential combinations of identigens to produce interpreted information344. The interpreted information 344 includes identification of at leastone equation package as a surviving interpretation SI 10, wherenon-surviving equation packages, if any, are eliminated that compareunfavorably to pairing rules to produce a non-surviving interpretation.

A fifth step of the generating the query response to the query includesutilizing a knowledge base, generating a query response to the survivingequation package of the query, where the surviving equation package ofthe query is transformed to produce query knowledge for comparison to aportion of the knowledge base. An answer is extracted from the portionof the knowledge base to produce the query response.

As depicted in FIG. 8K, a specific example of the fifth step includesthe answer resolution module 306 interpreting the survivinginterpretation SI 10 of the interpreted information 344 in accordancewith answer rules 322 to produce query knowledge QK 10 (i.e., agraphical representation of knowledge when the knowledge base utilizes agraphical database). For example, the answer resolution module 306accesses fact base information 600 from the SS memory 96 to identify theportion of the knowledge base associated with a favorable comparison ofthe query knowledge QK 10 (e.g., by comparing attributes of the queryknowledge QK 10 to attributes of the fact base information 600), andgenerates preliminary answers 354 that includes the answer to the query.For instance, the answer is “bat” when the associated OCAs of bat, suchas black, eats fruit, eats insects, is an animal, and flies, aligns withOCAs of the query knowledge.

FIG. 8L is a logic diagram of an embodiment of a method for generating aquery response to a query utilizing knowledge within a knowledge basewithin a computing system. In particular, a method is presented for usein conjunction with one or more functions and features described inconjunction with FIGS. 1-8D, 8J, and also FIG. 8K. The method includesstep 655 where a processing module of one or more processing modules ofone or more computing devices of the computing system identifies wordsof an ingested query to produce tokenized words. For example, theprocessing module compares words to known words of dictionary entries toproduce identifiers of known words.

For each tokenized word, the method continues at step 656 where theprocessing module identifies one or more identigens that correspond tothe tokenized word, where each identigen describes one of an object, acharacteristic, and an action. For example, the processing moduleperforms a lookup of identifiers of the one or more identigensassociated with each tokenized word, where different identifiersassociated with each permutation of a potential object, characteristic,and action associated with the tokenized word.

For each permutation of sequential combinations of identigens, themethod continues at step 657 where the processing module generates aplurality of equation elements to form a corresponding equation package,where each equation element describes a relationship betweensequentially linked pairs of identigens. Each sequential linking pairs apreceding identigen to a next identigen. For example, for eachpermutation of identigens of each tokenized word, the processing moduleincludes all other permutations of all other tokenized words to generatethe equation packages. Each equation package includes a plurality ofequation elements describing the relationships between sequentiallyadjacent identigens of a plurality of sequential combinations ofidentigens.

The method continues at step 658 where the processing module selects asurviving equation package associated with a most favorableinterpretation. For example, the processing module applies rules (i.e.,inference, pragmatic engine, utilizing the identifiers of the identigensto match against known valid combinations of identifiers of entigens) toreduce the number of permutations of the sequential combinations ofidentigens to identify at least one equation package. Non-survivingequation packages are eliminated the compare unfavorably to pairingrules.

The method continues at step 659 where the processing module generates aquery response to the surviving equation package, where the survivingequation package is transformed to produce query knowledge for locatingthe portion of a knowledge base that includes an answer to the query. Asan example of generating the query response, the processing moduleinterprets the surviving the equation package in accordance with answerrules to produce the query knowledge (e.g., a graphical representationof knowledge when the knowledge base utilizes a graphical databaseformat).

The processing module accesses fact base information from the knowledgebase to identify the portion of the knowledge base associated with afavorable comparison of the query knowledge (e.g., favorable comparisonof attributes of the query knowledge to the portion of the knowledgebase, aligning favorably comparing entigens without conflictingentigens). The processing module extracts an answer from the portion ofthe knowledge base to produce the query response.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 9A is a schematic block diagram of another embodiment of acomputing system that includes the content ingestion module 300 of FIG.5E, the element identification module 302 of FIG. 5E, and theinterpretation module 304 of FIG. 5E. Generally, an embodiment of theinvention presents solutions where the computing system functions tointerpret content. In an example of operation of the interpreting of thecontent, the content ingestion module 300 processes a phrase of words ofone or more of source content 310 and question content 312 to generateformatted content 314 that further includes a hypothesis token 660.

The hypothesis token 660 includes a parsing state 662, phrase word 664,and phrase interpretation result 666. The phrase words 664 includesunanalyzed words 668 and analyzed words 670. The phrase interpretationresult 666 includes most likely meanings 672 and unlikely meanings 674.The parsing state 662 indicates one or more of a number of words in thephrase, a number of analyzed words, a number of unanalyzed words, aninterpretation completeness level (e.g., percentage of words analyzed),and an interpretation quality level (e.g., number of most likelymeanings, number of interpretation loops traversed, etc.). Theunanalyzed word 668 includes a list of words that have not beentraversed in at least one or more analysis loops of the processing. Theanalyzed word 670 includes a list of words that have been traversed inthe at least one or more analysis loops of the processing.

The most likely meanings 672 includes one or more permutations of mostlikely meanings after the analysis traversed one or more loops of theprocessing. The unlikely meaning 674 includes any previously identifiedpossible interpretation that has been subsequently deemed as unlikely ina subsequent analysis loop of the processing. The generating of theformatted content 314 starts with generating the parsing state 662,identifying all words of an ingested phrase as unanalyzed, andinitiating the interpretation completeness level as incomplete.

The content ingestion module 300 initiates interpretation of the phrase.For example, the content ingestion module 300 issues formatted content314 to the element identification module 302, where the formattedcontent 314 includes the hypothesis token 660. While analyzing thephrase, the element identification module 302 identifies elements of theformatted content 314 in accordance with element rules 318 and anelement list 332 to produce identified element information 340, wherethe identifying includes updating the hypothesis token 660. For example,the element identification module 302 selects a number of unanalyzedwords, compares to the element list 332 in accordance with the elementrules 318 and when the comparison is favorable, produces the identifiedelement information 340 (e.g., update the phrase words 664 to indicateinterim analyzed words, update the parsing state 662).

While analyzing the phrase, the interpretation module 304 interprets theidentified element information 340 in accordance with one or more ofinterpretation rules 320, a groupings list 334, and question information346 to produce interpreted information 344, where the interpretingincludes updating the hypothesis token 660. For example, theinterpretation module 304 identifies a potential meaning when acomparison is favorable of some of the identified element information340 to the groupings list 334 and/or the question information 346 inaccordance with the interpretation rules 320, and for each potentialmeaning, generates a quality metric, identifies potential meaningsassociated with a favorable quality metric as most likely meanings 672and others as unlikely meaning 674, and updates the phrase word 664based on a number of words of the phrase analyze so far.

The element identification module 302 and/or the interpretation module304 determines whether to complete the analyzing of the phrase. Forexample, the element identification module 302 identifies theinterpretation complete list level and the interpretation quality levelof the parsing state from the hypothesis token 660, indicates that theanalysis has completed when the quality level is greater than a minimumquality threshold level or when the interpretation completed this levelis greater than a maximum completion partial level. The hypothesis token660 may traverse a plurality of loops of processing by the elementidentification module 302 and the interpretation module 304 prior toobtaining a favorable quality level.

FIG. 9B is a logic diagram of an embodiment of a method for optimizingingestion parsing within a computing system. In particular, a method ispresented for use in conjunction with one or more functions and featuresdescribed in conjunction with FIGS. 1-8D, 9A, and also FIG. 9B. Themethod includes step 680 where a processing module of one or moreprocessing modules of one or more computing devices of the computingsystem processes content to generate a hypothesis token corresponding toa phrase of the content. For example, the processing module generates aparsing state, identifies all words of an ingested phrase as unanalyzed,and initiates an interpretation completeness level as a complete.

The method continues at step 682 where the processing module initiatesinterpretation of the phrase. For example, the processing moduleprovides the hypothesis token for the interpretation, where thehypothesis token indicates that the interpretation complete list levelis incomplete and an interpretation quality level is less than a minimuminterpretation quality threshold level.

While analyzing the phrase, the method continues at step 684 where theprocessing module identifies elements of a portion of the phrase inaccordance with rules and an element list to produce identified elementinformation. The identifying includes one or more of selecting a numberof analyzed words, comparing to the element list (i.e., a dictionary) inaccordance with the rules, and when the comparison is favorable,producing the identified element information, and updating thehypothesis token to indicate interim analyzed words and an updatedperson state.

While analyzing the phrase, the method continues at step 686 where theprocessing module interprets the identified element information inaccordance with the rules and a grouping list to produce interpretedinformation. The interpreting includes one or more of identifying apotential meaning when a comparison is favorable of some of theidentified element information to the groupings list in accordance withthe rules, and for each potential meaning, generating a quality metric,identifying potential meanings associated with a favorable qualitymetric as most likely meanings and others as unlikely meanings, andupdating the hypothesis token based on a number of words of the phraseanalyzed so far.

The method continues at step 688 where the processing module determineswhether to complete the analyzing of the phrase. When the analyzing isnot complete the method loops back to step 684. When the analyzing iscomplete the method continues. The determining includes one or more ofidentifying the interpretation complete list level and theinterpretation quality level of the parsing state from the hypothesistoken, indicating that the analysis has completed when the quality levelis greater than a minimum quality threshold level or when theinterpretation completeness level is greater than a maximum completionlevel.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 10A is a schematic block diagram of another embodiment of acomputing system that includes a plurality of domain 1 (D1) throughdomain N (DN) content sources 700, a corresponding plurality of theartificial intelligence (AI) servers 20-1 through 20-N of FIG. 1, andthe user device 12-1 of FIG. 1. Each of the domain 1-N content sources700 includes content sources 16-1 through 16-N of FIG. 1. In particular,each of the AI servers 20-1 through 20-N is associated with acorresponding domain D1-DN of content sources 700. Each of the AIservers 20-1 through 20-N includes the processing module 50-1 of FIG. 2and the solid state (SS) memory 96 of FIG. 2, where each SS memory 96 isutilized to store associated domain knowledge. For instance, the SSmemory 96 of the AI server 20-1 is utilized to store domain 1 fact baseinformation 702, the AI server 20-2 is utilized to store domain 2 factbase information 702, etc.

Each processing module 50-1 includes the collections module 120 of FIG.4A, the identigen entigen intelligence (IEI) module 122 of FIG. 4A, andthe query module 124 of FIG. 4A. Generally, an embodiment of theinvention presents solutions where the computing system functions tohandoff a parsing process associated with parsing words of an ingestedphrase.

In an example of operation of the handing off of the parsing process,the IEI module 122 of the AI server 20-1 receives one or more IEIrequests 244 with regards to one or more query request 136 received bythe query module 124 from the user device 12-1, where the one or morequery request 136 are associated with one or more domains. The IEImodule 122 formats the one or more IEI requests 244 to produce ahypothesis token (e.g., as discussed with reference to FIGS. 9A-9B) thatincludes human expressions that includes question content and questioninformation associated with the one or more domains, where the producingof the human expressions is in accordance with expression identificationrules.

Having produced the hypothesis token, the IEI module 122 initiates IEIprocessing of the hypothesis token to produce one or more of interimknowledge, a preliminary answer, and an answer quality level. Forexample, the IEI module 122 identifies permutations of identigens of thehypothesis token, reduces the permutations, maps the reducedpermutations of identigens to entigens to produce the interim knowledge,processes the knowledge in accordance with the D1 fact base info 702 toobtained from the SS memory 96 to produce the preliminary answer andgenerate the answer quality level based on the preliminary answer forthe domain.

When the answer quality level associated with one or more of the queryrequests 136 is unfavorable, the IEI module 122 indicates to handoff thehypothesis token (e.g., identifies the domain associated with theunfavorable answer quality level). For example, the IEI module 122correlates the associated query request 136 with a domain associatedwith at least one of the other AI servers 20-2 through 20-N.

For each identified domain, the IEI module 122 sends the hypothesistoken 660 two one or more AI servers 20. For example, for each request,the IEI module 122 selects one or more of the servers that areassociated with the domain and/or have sufficient processing resources,and sends the current hypothesis token to the selected one or moreservers.

The IEI module 122 receives one or more matured hypothesis tokens 704,where one or more of the AI servers 20 responds to the one or morehypothesis token 660, where each matured hypothesis token 704 includesan improved quality level and/or completeness level of the correspondinghypothesis token 660. For example, AI server 20-2 produces acorresponding matured hypothesis token 704 that analyzed a correspondingportion of the hypothesis token 660 (e.g., of a domain associated withthe AI server 20-2) and the AI server 20-3 produces a correspondingmatured hypothesis token 704 that analyzed another corresponding portionof the hypothesis token 660 (e.g., of a domain associated with the AIserver 20-3).

Having received the one or more matured hypothesis token 704, the IEImodule 122 re-applies the IEI processing to the one or more (e.g., anaggregate) matured hypothesis token 704 to produce an updated answer andan updated answer quality level. When the updated answer quality levelsfavorable, the IEI module 122 issues an IEI response 246 to the querymodule 124 utilizing the updated answer, where the query module 124issues a query response 140 to the user device 12-1 based on the IEIresponse 246.

FIG. 10B is a logic diagram of an embodiment of a method for handing offa parsing process within a computing system. In particular, a method ispresented for use in conjunction with one or more functions and featuresdescribed in conjunction with FIGS. 1-8D, 10A, and also FIG. 10B. Themethod includes step 710 where a processing module of one or moreprocessing modules of one or more computing devices of the computingsystem interprets one or more received requests to produce a hypothesistoken that includes human expressions of questions and/or contentassociated with one or more domains. For example, the processing moduleanalyzes content of the one or more received requests in accordance withrules and generates the hypothesis token in accordance with a hypothesistoken structure.

The method continues at step 712 where the processing module initiatesIEI processing of the human expressions to produce one or more ofinterim knowledge, a preliminary answer, and an answer quality level.The initiating includes one or more of identifying permutations ofidentigens of the hypothesis token, reducing the permutations, mappingthe reduced permutations of identigens to entigens to produce theinterim knowledge, processing the knowledge in accordance with a factbase to produce the preliminary answer, and generating the answerquality level based on the preliminary answer for the domain.

When the IEI processing is unfavorable, the method continues at step 714where the processing module hands-off the hypothesis token. The handingoff includes one or more of identifying one or more domains associatedwith the unfavorable processing (e.g., poor quality, not enoughresources), selecting one or more processing resources (e.g., withsufficient processing capacity and/or that are associated with thedomains), and sending the hypothesis token to the one or more processingresources.

The method continues at step 716 with a processing module receives atleast one matured hypothesis token. The receiving includes receiving oneor more matured hypothesis tokens from the one or more processingresources, where a processing resource processes the hypothesis tokenutilizing local rules and a local fact base to identify meanings of thehuman expressions, where the meanings are associated with a favorablequality level, and where each matured hypothesis token includes animproved quality level and/or completeness level.

The method continues at step 718 where the processing module processesthe received at least one matured hypothesis token to produce one ormore of knowledge, an answer, and an updated answer quality level. Theprocessing includes one or more of extracting an interpreted meaningfrom a matured hypothesis token associated with a most favorable qualitylevel, aggregating two or more favorable matured hypothesis tokens toproduce an aggregated hypothesis token for extraction of the interpretedmeaning, and utilizing a first receive matured hypothesis token forextraction of the interpreted meaning.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 11A is a schematic block diagram of another embodiment of acomputing system that includes a plurality of domain 1 (D1) throughdomain N (DN) content sources 700, a corresponding plurality of theartificial intelligence (AI) servers 20-1 through 20-N of FIG. 1, andthe user device 12-1 of FIG. 1. Each of the domain 1-N content sources700 includes content sources 16-1 through 16-N of FIG. 1. In particular,each of the AI servers 20-1 through 20-N is associated with acorresponding domain D1-DN of content sources 700.

Each of the AI servers 20-1 through 20-N includes the processing module50-1 of FIG. 2 and the solid state (SS) memory 96 of FIG. 2, where eachSS memory 96 is utilized to store associated domain knowledge. Forinstance, the SS memory 96 of the AI server 20-1 is utilized to storedomain 1 fact base information 702, the AI server 20-2 is utilized tostore domain 2 fact base information 702, etc. Each processing module50-1 includes the collections module 120 of FIG. 4A, the identigenentigen intelligence (IEI) module 122 of FIG. 4A, and the query module124 of FIG. 4A. Generally, an embodiment of the invention presentssolutions where the computing system functions to enhance performance ofparsing an ingested phrase for processing to produce knowledge.

In an example of operation of the enhancing of the performance of theparsing, the IEI module 122 of the AI server 20-1 receives one or moreIEI requests 244 with regards to one or more query request 136 receivedby the query module 124 from the user device 12-1, where the one or morequery request 136 are associated with one or more domains. The IEImodule 122 formats the one or more IEI requests 244 to produce ahypothesis token (e.g., as discussed with reference to FIGS. 9A-9B) thatincludes human expressions that includes question content and questioninformation associated with the one or more domains, where the producingof the human expressions is in accordance with expression identificationrules.

Having produced the hypothesis token, the IEI module 122 initiates IEIprocessing of the hypothesis token to produce one or more of interimknowledge, a preliminary answer, and an answer quality level. Forexample, the IEI module 122 identifies permutations of identigens of thehypothesis token, reduces the permutations, maps the reducedpermutations of identigens to entigens to produce the interim knowledge,processes the knowledge in accordance with the D1 fact base info 702 toobtained from the SS memory 96 to produce the preliminary answer andgenerate the answer quality level based on the preliminary answer forthe domain.

When the answer quality level associated with one or more of the queryrequests 136 is unfavorable, the IEI module 122 indicates to parallelprocess the hypothesis token (e.g., identifies the domain associatedwith the unfavorable answer quality level). For example, the IEI module122 correlates the associated query request 136 with a domain associatedwith at least one of the other AI servers 20-2 through 20-N.

The IEI module 122 sends the hypothesis token 660 to one or more otherAI servers 20. For example, the IEI module 122 selects one or more ofthe servers that are associated with the domain and/or have sufficientprocessing resources, and sends the current hypothesis token to theselected one or more servers, where the hypothesis token 660 mayindicate which server is to operate on which portion of remaining wordsfor analysis.

The IEI module 122 receives one or more matured hypothesis tokens 704,where one or more of the AI servers 20 responds to the one or morehypothesis token 660, where each matured hypothesis token 704 includesan improved quality level and/or completeness level of the correspondinghypothesis token 660. For example, the AI server 20-3 and the AI server20-2 produce corresponding matured hypothesis token 704 associated witha common domain in accordance with instructions in the hypothesis token660 which indicate which server is to operate on which portion of theremaining words of the common domain for analysis.

Having received the one or more matured hypothesis token 704, the IEImodule 122 re-applies the IEI processing to the one or more (e.g., anaggregate) matured hypothesis tokens 704 to produce an updated answerand an updated answer quality level. When the updated answer qualitylevels favorable, the IEI module 122 issues an IEI response 246 to thequery module 124 utilizing the updated answer, where the query module124 issues a query response 140 to the user device 12-1 based on the IEIresponse 246.

FIG. 11B is a logic diagram of an embodiment of a method for enhancingperformance of parsing an ingested phrase within a computing system. Inparticular, a method is presented for use in conjunction with one ormore functions and features described in conjunction with FIGS. 1-8D,11A, and also FIG. 11B. The method includes step 730 where a processingmodule of one or more processing modules of one or more computingdevices of the computing system interprets one or more received requeststo produce a hypothesis token that includes human expressions ofquestions and/or content associated with one or more domains. Forexample, the processing module analyzes content of the one or morereceived requests in accordance with rules and generates the hypothesistoken in accordance with a hypothesis token structure.

The method continues at step 732 where the processing module initiatesJET processing of the human expressions to produce one or more ofinterim knowledge, a preliminary answer, and an answer quality level.The initiating includes one or more of identifying permutations ofidentigens of the hypothesis token, reducing the permutations, mappingthe reduced permutations of identigens to entigens to produce theinterim knowledge, processing the knowledge in accordance with a factbase to produce the preliminary answer, and generating the answerquality level based on the preliminary answer for the domain.

When the JET processing is unfavorable, the method continues at step 734with a processing module distributes the hypothesis token to a pluralityof processing resources. The distributing includes one or more ofidentifying one or more domains associated with the unfavorableprocessing (e.g., poor quality, not enough resources), selecting theplurality of processing resources (e.g., sufficient capacity,knowledgebase associated with one or more of the domains), mapping aportion of unprocessed human expressions to each of the plurality ofprocessing resources (e.g., by domain, by resource availability,sequentially, etc.), and sending the hypothesis token to the pluralityof processing resources in accordance with the mapping.

The method continues at step 736 where the processing module receives aplurality of matured hypothesis tokens. The receiving includes one ormore of receiving the plurality of matured hypothesis tokens from theplurality of processing resources, where a processing resource processesthe hypothesis token utilizing local rules in a local fact base toidentify means of human expressions in accordance with the mapping,where the meetings are associated with a favorable quality level, andwhere each matured hypothesis token includes an improved quality and/orcompleteness level.

The method continues at step 738 where the processing module aggregatesthe matured hypothesis tokens to produce one or more of knowledge, ananswer, and an updated answer quality level. For example, the processingmodule combines two or more favorable matured hypothesis tokensassociated within a common domain to produce an aggregated hypothesistoken for extraction of the interpreted meaning.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 12A is a schematic block diagram of another embodiment of acomputing system that includes the element identification module 302 ofFIG. 5E and the interpretation module 304 of FIG. 5E. The elementidentification module 302 includes the element matching module 400 ofFIG. 6A and the element grouping module 402 of FIG. 6A. Theinterpretation module 304 includes the grouping matching module 404 ofFIG. 6A and the grouping interpretation module 406 of FIG. 6A.Generally, an embodiment of the invention presents solutions where thecomputing system functions to classify prepositional phrases of ingestedcontent when processing the content to produce knowledge.

In an example of operation of the classifying of the prepositionalphrases, when matching elements of received formatted content 314, theelement matching module 400 generates matched elements 412. Thegenerating includes matching a received element to an element of anelement list 332, where the element list 332 further includesidentification of prepositions, and outputting the matched elements 412to includes an identifier of the matched element.

The element grouping module 402 analyzes the matched elements 412 inaccordance with element rules 318, that further includes prepositionrules, produces identified element information 340 when favorablestructures associated with the matched elements 412 in accordance withthe element rules 318, and produces preposition information 750 (e.g.,identify prepositions based on lists and rules, permutations of possiblepreposition meanings (i.e., based on sense such as spatial, temporal,possession, etc.). An example of analyzing, the element grouping module402 compares matched elements 412 with structure and element rules 318and extracts possible preposition meanings from the element rules 318.

The group matching module 404 analyzes the identified elementinformation 340 and preposition information 750 in accordance with agrouping list 334 to produce validated groupings information 416. Theproducing includes one or more of comparing a groupings aspect of theidentified element information 340 in light of the prepositioninformation 750 (e.g., for each permutation of groups of elements ofpossible interpretations), and generates the validated groupingsinformation 416 to include identifications of valid permutations thatalign with the groupings list 334 in light of the prepositioninformation 750.

The grouping interpretation module 406 interprets the validatedgroupings information 416 based on the question information 346 and inaccordance with interpretation rules 320 to produce interpretedinformation 344 (e.g., most likely interpretations, next likelyinterpretations, etc.). The producing may be based on the plurality ofpossible meetings of a given preposition and may include pruning theplurality of possible meetings based on the interpretation rules 320 inlight of other words of the validated groupings information 416 of thephrase (e.g., eliminate a meaning when the preposition and other wordsaround and do not align with the meaning of the preposition) andoutputting an interpretation of the phrase that includes a meaning ofthe preposition that survives the pruning.

FIG. 12B is a logic diagram of an embodiment of a method for classifyingprepositional phrases within a computing system. In particular, a methodis presented for use in conjunction with one or more functions andfeatures described in conjunction with FIGS. 1-8D, 12A, and also FIG.12B. When matching elements of received formatted content of an ingestedphrase of words, the method includes step 760 where a processing moduleof one or more processing modules of one or more computing devices ofthe computing system generates matched elements in accordance with anelement list, where the matched elements includes at least onepreposition and where the element list includes prepositions. Forexample, the processing module matches a received element to an elementof the element list, where the element list further includesidentification of prepositions, and outputs the matched elements toinclude an identifier of each matched element.

The method continues at step 762 where the processing module analyzesthe matched elements in accordance with element rules to producepreposition information and identified element information, where theelement rules includes preposition rules. The analyzing includes one ormore of identifying prepositions from the matched elements based onpreposition rules and/or the element list, identifying permutations ofpossible preposition meetings (e.g., sense such as spatial, temporal,position, etc.) based on the preposition rules, outputting theidentified prepositions and identified permutations of possiblepreposition meanings as the preposition information, and outputtingother words of the phrase is identified element information.

The method continues at step 764 where the processing module analyzesthe identified element information and preposition information inaccordance with a groupings list to produce validated groupingsinformation. The analyzing includes one or more of comparing a groupingsaspect of the identified element information in light of the prepositioninformation (e.g., for each permutation of groups of elements ofpossible interpretations) and generating the validated groupingsinformation to include identification of valid permutations that alignwith the groupings list in light of the preposition information.

The method continues at step 766 with a processing module interprets thevalidated groupings information in accordance with interpretation rulesto produce interpreted information, where in valid permutations ofpreposition meanings have substantially been illuminated. Theinterpreting includes one or more of utilizing the plurality of possiblemeanings of a given preposition, pruning the plurality of possiblemeanings based on the interpretation rules in light of other words ofthe validated groupings information of the phrase (e.g., eliminate ameaning when the preposition and other words around it do not align withthe meaning of the preposition), and outputting an interpretation of thephrase that includes a meaning of the preposition that survives thepruning.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 13A is a schematic block diagram of another embodiment of acomputing system that includes the AI server 20-1 of FIG. 1 and the AIserver 20-2 of FIG. 1. Each of the AI servers 20-1 and 20-2 include theprocessing module 50-1 of FIG. 2 and the solid state (SS) memory 96 ofFIG. 2, where each SS memory 96 is utilized to store associated domainknowledge. For instance, the SS memory 96 of the AI server 20-1 isutilized to store domain 1 (D1) fact base information 702, the AI server20-2 is utilized to store D2 fact base information 702, etc. Eachprocessing module 50-1 includes the collections module 120 of FIG. 4A,the identigen entigen intelligence (IEI) module 122 of FIG. 4A, and thequery module 124 of FIG. 4A. Generally, an embodiment of the inventionpresents solutions where the computing system functions to create asuperset of knowledge.

In an example of operation of the creating of the superset of knowledge,the IEI module 122 of the AI server 20-1 generates a portion of theknowledge base (e.g., a subset, a topic area, an area of interest, aquery with an unfavorably low amount of related knowledge), where theknowledge base is associated with the IEI module 122. For example, theIEI module 122 receives content from one or more of the collectionsmodule 120 and the query module 124, IEI processes the content toproduce incremental knowledge, aggregates the criminal knowledge withknowledge of the knowledge base (e.g., stores the knowledge as D1fact-base info 702 in the SS memory 96.

The IEI module 122 determines that the portion of knowledge base has anunfavorably low amount of related knowledge. The determining includesone or more of obtaining the portion from the SS memory 96, analyzingthe portion to produce a knowledge breadth level, indicating anunfavorable level when the knowledge breadth level is less than aminimum knowledge breadth threshold level.

The IEI module 122 obtains a subset of a likely similar portion ofanother knowledgebase (e.g., another artificial entity, an anthologythat utilizes resource descriptor frameworks, etc.). The obtainingincludes identifying the other knowledgebase (e.g., interpreting thelist, interpreting a query), issuing a query request and interpreting aquery response from the identified other knowledgebase (e.g., sending aknowledge request 770 to the AI server 20-2 and receiving a knowledgeresponse 772 that includes the likely similar portion of the otherknowledgebase).

The IEI module 122 compares the likely similar portion of the otherknowledgebase to the portion of the knowledgebase to produce asimilarity level. The comparing includes comparing meaninginterpretations by identified domains and topics. Having produced thesimilarity level, the IEI module 122 obtains substantially all remainingsubsets of the similar portion of the other knowledgebase when thesimilarity level is greater than a similarity threshold level. Forexample, the IEI module 122 issues another knowledge request 770 to theAI server 20-2 requesting the substantially all remaining subsets of thesimilar portion of the other knowledgebase and receiving anotherknowledge response 772 that includes the further knowledge.

Having obtained the remaining subsets of the similar portion of theother knowledgebase, the IEI module 122 unions the portion of theknowledgebase with the remaining subsets by ingesting new knowledge fromthe remaining subsets and adding the new knowledge to the knowledgebaseassociated with the AI engine. For example, the IEI module 122 of the AIserver 20-1 aggregates the portion of the knowledgebase from SS memory96 of the AI server 20-1 with additional knowledge extracted from theknowledge response 772 received from the AI server 20-2.

FIG. 13B is a logic diagram of an embodiment of a method for creating asuperset of knowledge within a computing system. In particular, a methodis presented for use in conjunction with one or more functions andfeatures described in conjunction with FIGS. 1-8D, 13A, and also FIG.13B. The method includes step 780 where a processing module of one ormore processing modules of one or more computing devices of thecomputing system identifies a portion of a knowledgebase. Theidentifying includes one or more of receiving an identifier,interpreting a query response, receiving content, generating the portion(e.g., by a subset, topic area, an area of interest to produceincremental knowledge), accessing the knowledge base, and aggregatingincremental knowledge with knowledge of the knowledgebase to generateupdated knowledgebase information.

The method continues at step 782 where the processing module determinesthat the portion of the knowledgebase has an unfavorably low level ofrelated knowledge. The determining includes one or more of obtaining theportion from the knowledgebase, analyzing the portion to produce aknowledge breadth level, and indicating unfavorable when the knowledgebreadth level is less than a minimum knowledge breadth threshold level.

The method continues at step 784 with a processing module obtains asubset of a likely similar portion of another knowledgebase. Theobtaining includes one or more of identifying the other knowledgebase(e.g., interpret a list, interpret a query), issuing a query request,and interpreting a query response from the identified other knowledgebases (e.g., send a knowledge request to the other knowledgebase andreceiving knowledge response that includes the likely similar portion ofthe other knowledgebase).

The method continues at step 786 where the processing module comparesthe likely similar portion of the other knowledgebase to the portion ofthe knowledgebase to produce a similarity level. The comparing includesone or more of obtaining meaning interpretations (e.g., interpretedmeanings for some similar knowledge) for similar domain and identifytopics, and comparing meanings to produce the similarity level.

The method continues at step 788 with a processing module obtainsremaining subsets of the similar portion of the other knowledgebase whenthe similarity level is greater than a minimum similarity thresholdlevel. For example, the processing module issues a request and extractsknowledge from a response to the request. The method continues at step790 where the processing module unions the portion of the knowledgebasewith the remaining subsets by ingesting new knowledge from the remainingsubsets and adding the new knowledge to the knowledgebase. For example,the processing module aggregates knowledge when in a common format andeliminates duplicate knowledge.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIGS. 13C-13E are schematic block diagrams of another embodiment of acomputing system illustrating another example of creating a superset ofknowledge within a computing system. The computing system includes thecontent ingestion module 300 of FIG. 5E, the element identificationmodule 302 of FIG. 5E, the interpretation module 304 of FIG. 5E, theanswer resolution module 306 of FIG. 5E, the user device 12-1 of FIG. 1,a first knowledge database 830-1 and a second knowledge database 830-2.In another embodiment, the user device 12-1 is replaced by one or moreof the content sources 16-1 through 16-N of FIG. 1. In an embodiment,the first and second knowledge databases are implemented utilizing thefact base 592 of FIG. 8A.

FIG. 13C illustrates an example method operation of the creating thesuperset of knowledge, where, a first step includes the contentingestion module 300 obtaining content for a topic (e.g., bats) thatincludes a plurality of words. For example, the content ingestion module300 receives the content 832 “black bat eats fruit” and parses thecontent 832 to produce phrase words 792 (e.g., “black”, “bat” “eats”,“fruit”). The parsing includes one or more of performing a dictionarylook up to verify each word of the plurality of words, substitutingwords within a common language, and substituting words betweenlanguages.

Having obtained the content, a second step of the example method ofoperation includes the element identification module 302 determining aset of identigens for each word of the plurality of words of the contentto produce a plurality of sets of identigens 796. Each identigen of theset of identigens includes a meaning identifier, an instance identifier,and a time reference. Each meaning identifier associated with aparticular set of identigens represents a different meaning of one ormore different meanings of a corresponding word of the plurality ofwords of the content. Each time reference provides time information whena corresponding different meaning of the one or more different meaningsis valid. A set of identigens of the plurality of sets of identigens isproduced for a first word of the plurality of words of the content.

The determining the set of identigens for each word includes the elementidentification module 302 accessing the first knowledge database 830-1utilizing the phrase words 792 to obtain identigen information 794 fromthe first knowledge database 830-1. The element identification module302 interprets the identigen information 794 to produce the sets ofidentigens 796. For example, a second identigen set for the word “bat”includes identigens 4-6 corresponding to 3 different instances of 3meanings of the word “bat” (e.g., a baseball bat, a flying bat, and tohit).

Having produced the sets of identigens 796, a third step of the examplemethod of operation includes the interpretation module 304 interpreting,in accordance with identigen pairing rules 798 of the first knowledgedatabase a 830-1, the plurality of sets of identigens 796 to determine amost likely meaning interpretation of the content and produce a baselineentigen group 833 comprising a plurality of baseline entigens forstorage in the first knowledge database 830-1. The first knowledgedatabase 830-1 includes a first multitude of entigen groups associatedwith a first multitude of topics. The first multitude of topics includesthe topic. Each entigen group of the first multitude of entigen groupsincludes a corresponding plurality of entigens and one or more entigenrelationships between at least some of the corresponding plurality ofentigens.

The baseline entigen group represents the most likely meaninginterpretation of the content. Each baseline entigen of the baselineentigen group corresponds to a selected identigen of the set ofidentigens having a selected meaning of the one or more differentmeanings of each word of the plurality of words. Each baseline entigenof the baseline entigen group represents a single conceivable andperceivable thing in space and time that is independent of language andcorresponds to a time reference of the selected identigen associatedwith the baseline entigen group.

The selected identigen favorably pairs with at least one correspondingsequentially adjacent identigen of another set of identigens of theplurality of sets of identigens based on the identigen pairing rules ofthe first knowledge database. For example, the interpretation module 304interprets identigen rules 798 recovered from the first knowledgedatabase 830-1 with regards to the sets of identigens 796 to produce thebaseline entigen group 833 linking entigens 2, 5, 8, 9 for the mostlikely meanings of the words “black bat eats fruit.”

The third step of the example method of operation further includes theinterpretation module 304 storing the baseline entigen group 833. Forexample, the interpretation module 304 outputs the baseline entigengroup 833 to the first knowledge database 830-1 for subsequentutilization as knowledge pertaining to the topic of bats.

FIG. 13D further illustrates the example method of operation of thecreating of the superset of knowledge where, having produce the baselineentigen group for storage in the first knowledge database 830-1, afourth step includes the interpretation module 304 determining aknowledge defect of an incomplete entigen group 835 with regards to thetopic. The determining includes a variety of approaches. A firstapproach includes determining that a number of entigens of theincomplete entigen group is less than a minimum number of entigensthreshold number. For example, the interpretation module 304 interpretsfirst entigen information 834 from the first knowledge database 830-1 todetermine that too few entigens exist in a particular entigen groupregarding the topic. For instance, the interpretation module 304identifies the knowledge defect as too few entigens when the number ofentigens (e.g., 4) of the entigen group representing “black bat eatsfruit” is less than a minimum number of entigens threshold of ten.

A second approach includes determining that the incomplete entigen groupdoes not contain an expected yet missing entigen of an expectedcategory. For example, the interpretation module 304 identifies theknowledge defect as a missing type entigen when a type of bat isexpected.

A third approach includes determining that the incomplete entigen groupdoes not contain an expected yet missing entigen relationship betweenfirst and second entigens of the incomplete entigen group. For example,the interpretation module 304 identifies the knowledge defect as themissing entigen relationship when a link between the bat entigen and thetype entigen has not been defined.

The fourth step of the example method of operation further includes theinterpretation module 304 recovering the incomplete entigen group 835for the topic from the first knowledge database 830-1 based on theknowledge defect of the incomplete entigen group with regards to thetopic. The incomplete entigen group 835 includes at least some of theplurality of baseline entigens of the baseline entigen group 833. TheIncomplete entigen group 835 includes a plurality of incomplete entigensand one or more entigen relationships between at least some of theplurality of incomplete entigens. The incomplete entigen group 835represents at least some knowledge of the topic.

The recovering the incomplete entigen group 835 for the topic from thefirst knowledge database 830-1 based on the knowledge defect of theincomplete entigen group with regards to the topic includes a series ofsub-steps. A first sub-step includes obtaining a subject entigen groupfrom the first knowledge database that includes at least some of theplurality of baseline entigens of the baseline entigen group. Forexample, the interpretation module 304 extracts the baseline entigengroup 833 from first entigen information 834 recovered from the firstknowledge database 830-1 to produce the subject entigen group withregards to the topic of bats.

A second sub-step includes identifying the knowledge defect of thesubject entigen group. For example, the interpretation module 304identifies the knowledge defect to be the missing type of bat entigenand relationship to the bat entigen as previously discussed.

A third sub-step includes establishing the subject entigen group as theincomplete entigen group and the knowledge defect of the subject entigengroup as the knowledge defect of the incomplete entigen group. Forexample, the interpretation module 304 establishes the incompleteentigen group 835 to include the baseline entigen group 833 and aplaceholder for the type entigen link to the bat entigen.

Having recovered the incomplete entigen group 835, a fifth step of theexample method of operation of creating the superset of knowledgeincludes obtaining an additive entigen group 837 from the secondknowledge database 830-2 based on the knowledge defect of the incompleteentigen group with regards to the topic. The second knowledge database830-2 includes a second multitude of entigen groups associated with asecond multitude of topics. The second multitude of topics includes thetopic. Each entigen group of the second multitude of entigen groupsincludes a corresponding plurality of entigens of the second multitudeof entigen groups and one or more entigen relationships between at leastsome of the corresponding plurality of entigens of the second multitudeof entigen groups.

The obtaining the additive entigen group 837 from the second knowledgedatabase 830-2 based on the knowledge defect of the incomplete entigengroup with regards to the topic includes a series of sub-steps. A firstsub-step includes identifying at least one of a missing entigen and amissing entigen relationship of the knowledge defect of the incompleteentigen group as previously discussed. For example, the interpretationmodule 304 identifies the type entigen link to the bat entigen asmissing.

A second sub-step includes obtaining a candidate entigen group from thesecond knowledge database 830-2 that includes at least some of theplurality of incomplete entigens of the incomplete entigen group. Forexample, the interpretation module 304 extracts the candidate entigengroup from second entigen information 836 from the second knowledgedatabase 830-2 with regards to the bat topic, where a mammal entigennumber 12 provides the missing entigen.

A third sub-step includes determining that the candidate entigen groupfurther includes a solution for the at least one of the missing entigenand the missing entigen relationship of the knowledge defect of theincomplete entigen group. For example, the interpretation module 304extracts the candidate entigen group from the second entigen information836 to include the link between the mammal entigen and the bat entigenalong with an additional animal entigen number 13 describing a type ofmammal for the bat.

A fourth sub-step includes establishing the candidate entigen group asthe additive entigen group. For example, the interpretation module 304upon verifying the missing entigen and missing link solutions indicatesthat the candidate entigen group is the additive entigen group 837,including entigens for linked meanings of “black bat mammal animal.”

FIG. 13E further illustrates the example method of operation of creatingthe superset of knowledge, where, having obtained the additive entigengroup, a sixth step includes the answer resolution module 306 modifyingthe incomplete entigen group 835 utilizing the additive entigen group837 to produce an updated entigen group 838 to provide a beneficial curefor the knowledge defect of the incomplete entigen group. The modifyingthe incomplete entigen group utilizing the additive entigen group toproduce the updated entigen group includes a series of sub-steps. Afirst sub-step includes identifying at least one of a missing entigenand a missing entigen relationship of the knowledge defect of theincomplete entigen group as previously discussed.

A second sub-step includes extracting a solution for the at least one ofthe missing entigen and the missing entigen relationship of theknowledge defect of the incomplete entigen group from the additiveentigen group. For example, the answer resolution module 306 extractsthe mammal entigen number 12 and the linked animal entigen number 13from the additive entigen group.

A third sub-step includes supplementing the incomplete entigen groupwith the solution to produce the updated entigen group. For example, theanswer resolution module 306 links the mammal entigen to the bat entigenand links the animal entigen to the mammal entigen to produce theupdated entigen group 838.

Having produced the updated entigen group, a seventh step of the examplemethod of operation creating the superset of knowledge includes theanswer resolution module 306 performing one or more of a variety ofsub-steps. A first sub-step includes the answer resolution module 306storing the updated entigen group 838 in the first knowledge database830-1 to replace the incomplete entigen group 835 augmenting thebaseline entigen group 833.

A second sub-step includes the answer resolution module 306 storing theupdated entigen group 838 in the second knowledge database 830-2 toreplace the additive entigen group 837. For example, the answerresolution module 306 updates the additive entigen group in the secondknowledge database 830-2 to add the entigens with regards to bats eatingfruit.

A third sub-step includes the answer resolution module 306 outputting,via a user interface of the answer resolution module 306, arepresentation 839 of the updated entigen group with an indication of acurated status. For example, the answer resolution module 306 convertsthe updated entigen group 838 into plaintext (e.g., black bat eats fruitand is a mammal animal) and outputs the representation 839 of theupdated entigen group (e.g., that includes the updated entigen group andthe status indicating curated knowledge) to the user device 12-1.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element, a sixth memoryelement, a seventh memory element, etc.) that stores operationalinstructions can, when executed by one or more processing modules of oneor more computing devices (e.g., one or more servers, one or more userdevices) of the computing system 10, cause the one or more computingdevices to perform any or all of the method steps described above.

FIGS. 14A, 14D, 14E, and 14F are schematic block diagrams of anotherembodiment of a computing system illustrating an example of a method forinterpreting a meaning of a word string. The computing system includesthe content ingestion module 300 of FIG. 5E, the element identificationmodule 302 of FIG. 5E, the interpretation module 304 of FIG. 5E, and aknowledge database 800. The knowledge database 800 may be implementedutilizing one or more of the memories of FIG. 2.

FIG. 14A illustrates the example of the method for interpreting themeaning of the word string where the content ingestion module 300obtains a string of words 810. The obtaining includes receiving wordsand generating the string of words based on source content 310. Forexample, the content ingestion module 310 extracts words A-E fromreceived source content 310. As another example, the content ingestionmodule 310 generates the words A-E based on a received paragraph ofsource content 310.

Having received the string of words, the element identification module302 determines whether two or more words of the string of words are in aword group. A word group includes two or more words that are frequentlyfound together in accordance with a particular language and represent atleast one meaning for the word group. As a simple example, a string ofwords that includes “the black cat ran” includes a word group of “blackcat” as that word group is frequently found together and can beinterpreted as a singular word group.

The determining of whether the two or more words of the string of wordsare in the word group includes at least two approaches. In a firstapproach, the element identification module 302 accesses word groupinformation 802 of the knowledge database 800 to attempt to match a wordof the string of words with an entry word of the word group information802 to identify possible word groups. In a second approach, the elementidentification module 302 accesses the word group information 802 toattempt to match two or more words of the string of words to acorresponding two or more entry words of the word group information 802.For example, the element identification module 302 matches words B-C-Dof the string of words 810 to entry words BCD of the word groupinformation 802 utilizing the second approach and matches the words B-Cof the string of words 810 to entry words BC of the word groupinformation 802.

Having determined that the two or more words of the string of words arein the word group, the element identification module 302 outputs theidentified word group 812 and remaining words 814 of the string ofwords. For example, the element identification module 302 outputs wordgroup B-C-D a as the word group 812 and words A and E as the remainingwords 814. Alternatively, or in addition to, the element identificationmodule 302 outputs the word group B-C and the remaining words 814 toinclude words A, D, and E. The approaches to determine whether the twoor more words of the string of words are in the word group are discussedin greater detail with reference to FIGS. 14B and 14C.

FIGS. 14B and 14C are data flow diagrams illustrating the continuedexample of the method for interpreting the meaning of the word string.FIG. 14B illustrates a method of the first approach to identify whetherthe two or more words of the string of words are in the word group thatincludes detecting that a first word of the two or more words of thestring of words matches a first entry word of one or more word groupentries of a word group list of the word group information 802. Eachword group entry of the word group list includes two or more entry wordsand an entry set of word group identigens that corresponds tointerpretations of the two or more entry words. The two or more entrywords of each of the one or more word group entries includes the firstentry word. For example, a match occurs between word B and four entriesof the word group list where entry word B is the first entry word.

Having detected the first word match, the method further includesdetermining, for at least some of the one or more word group entries,whether remaining words of the of the two or more words of the string ofwords match remaining entry words of the two or more entry words. Forexample, remaining word C matches entry word C of the first identifiedentry of the word group list. As another example, remaining words C andD match entry words C and D of the fourth identified entry of the wordgroup list.

When the remaining words of the of the two or more words of the stringof words match the remaining entry words of the two or more entry wordsof a matching word group entry of the at least some of the one or moreword group entries, the method further includes indicating that the twoor more words of the string of words are in the word group. For example,the words B and C of the string of words are indicated to be in the wordgroup B-C and the words B, C, and D of the string of words are indicatedto be in the word group B-C-D as another alternative.

FIG. 14C illustrates a method of the second approach to identify whetherthe two or more words of the string of words are in the word group thatincludes comparing the two or more words of the string of words to aword group entry of the word group list. The word group entry of theword group list includes two or more entry words and an entry set ofword group identigens that corresponds to potential interpretations ofthe two or more entry words. For example, while no word groups areidentified to include the word A, words B and C of the string of wordsare matched to entry words B and C of the word group list as a two wordmatch. As another example, words B, C, and D of the string of words aredirectly match to entry words B, C, and D of the word group list as athree word match.

Having matched two or more words of the string of words to at least oneword group, the method further includes indicating that the two or morewords of the string of words are in the word group when the two or morewords of the string of words compare favorably to the two or more entrywords of the entry of the word group list. For example, the methodincludes indicating that the words B and C are in the word group B-C ofthe word group list. As another example, the method includes indicatingthat words B, C, and D are in the word group B-C-D of the word grouplist.

FIG. 14D further illustrates the continued example of the method forinterpreting the meaning of the word string where, when the two or morewords are in the word group, the interpretation module 304 retrieves aset of word group identigens for the word group. For example, theinterpretation module 304 retrieves the word group identigens 804 forthe BCD set of word group identigens from the word group information802. Each word group identigen represents a different meaning of theword group. For example, the interpretation module 304 retrieves twoword group identigens iBCD1 and iBCD2 for the word group BCD when twomeanings are listed for the word group BCD and when the interpretationmodule 304 determines to utilize a largest word group when the elementidentification module 302 presents multiple alternatives of word groups.

The determining of the size of the desired word group is based on one ormore of a predetermination, a number of word group identigens for eachword group, and a number of remaining words of the string of words. Forexample, the interpretation module 304 determines to utilize thethree-word word group when the number of word group identigens of thetwo-word word group is less than the number of word group identigens ofthe three-word word group.

FIG. 14E further illustrates the continued example of the method forinterpreting the meaning of the word string where, when the two or morewords are in the word group, the interpretation module 304 retrieves aplurality of sets of word identigens for remaining words of the stringof words. For example, the interpretation module 304 retrieves wordidentigens 808 from word information 806 that includes word identigensiA1 and iA2 for the word A when the set of word identigens for word Aincludes two word identigens. As another example, the interpretationmodule 304 retrieves word identigens 808 from the word information 806that includes word identigen iE1 for the word E when the set of wordidentigens for word E includes a single word identigen.

FIG. 14F further illustrates the continued example of the method forinterpreting the meaning of the word string where when the two or morewords are in the word group, the interpretation module 304 determineswhether a word group identigen of the set of word group identigens and aplurality of word identigens of the plurality of sets of word identigenscreates an entigen group that is a valid interpretation of the string ofwords. When the entigen group is the valid interpretation of the stringof words, the interpretation module 304 outputs the entigen group 816.

The determining whether the word group identigen of the set of wordgroup identigens and the plurality of word identigens of the pluralityof sets of word identigens creates the entigen group that is the validinterpretation of the string of words includes a series of steps. Afirst step includes interpreting, based on the knowledge database 800,the set of word group identigens and the plurality of sets of wordidentigens to produce an intermediate identigen group. For example, theinterpretation module 304 produces the intermediate identigen group toinclude intermediate identigens iA1, iBCD2, and iE1 as a potentialpermutation of several permutations of identigens.

The intermediate identigen group is potentially a most likelyinterpretation of the string of words. Each intermediate identigen ofthe intermediate identigen group corresponds to a selected identigen ofone of the set of word group identigens and the plurality of wordidentigens of the plurality of sets of word identigens. Each selectedidentigen represents a most likely meaning of one of the two or morewords of the string of words and one remaining word of the string ofwords. The knowledge database includes a plurality of records that linkmeanings of words having a connected meaning.

A second step to determine creation of the entigen group includesdetermining whether the intermediate identigen group includes oneselected word group identigen of the set of word group identigens. Forexample, the interpretation module 304 confirms that intermediateidentigen group iBCD2 has been selected.

A third step to determine creation of the entigen group includesdetermining whether the intermediate identigen group includes a subsetof intermediate identigens corresponding to the plurality of sets ofword identigens, where the remaining words of the string of wordscorresponds to the subset of intermediate identigens. For example, theinterpretation module 304 confirms that intermediate identigens iA1 andiE1 were selected for the remaining words.

A fourth step to determine creation of the entigen group includesdetermining whether a sequencing of each intermediate identigen is inaccordance with identigen sequencing rules 809 of the knowledgedatabase. For example, the interpretation module 304 confirms validityof sequencing of iBCD2 after iA1 and iE1 after iBCD2.

A fifth step to determine creation of the entigen group includes, whenthe intermediate identigen group includes one selected word groupidentigen of the set of word group identigens and the intermediateidentigen group includes the subset of intermediate identigens thatcorrespond to the plurality of sets of word identigens and thesequencing of each intermediate identigen is in accordance with theidentigen sequencing rules of the knowledge database, generating theentigen group 816 that is the valid interpretation of the string ofwords utilizing the intermediate identigen group. For example, theinterpretation module 304 generates the entigen group 816 to includeentigens eA1, eBCD2, and eE1.

When the intermediate identigen group does not include one selected wordgroup identigen of the set of word group identigens or the intermediateidentigen group does not include the subset of intermediate identigensthat correspond to the plurality of sets of word identigens or thesequencing of each intermediate identigen is not in accordance with theidentigen sequencing rules of the knowledge database, then theinterpretation module 304 reverts to interpreting individual wordidentigens for each word of the string of words. For example, theinterpretation module 304 obtains an updated plurality of sets of wordidentigens for all words of the string of words (e.g., additional wordidentigens for each of the words B, C, and D.

The interpretation module 304 interprets, based on the knowledgedatabase 800, the updated plurality of sets of word identigens toproduce the entigen group (e.g., in accordance with identigen sequencingrules 809). The Entigen group is a most likely interpretation of thestring of words and each entigen of the entigen group corresponds to aselected word identigen of the updated plurality of sets of wordidentigens. Each selected word identigen represents a most likelymeaning of a word of the string of words. Having produced the entigengroup, the interpretation module 304 indicates that the entigen group isthe valid interpretation of the string of words.

The method described above can alternatively be performed by othermodules of the computing system 10 of FIG. 1 or by other devices. Inaddition, at least one memory section (e.g., a computer readable memory,a non-transitory computer readable storage medium, a non-transitorycomputer readable memory organized into a first memory element, a secondmemory element, a third memory element, a fourth element section, afifth memory element etc.) that stores operational instructions can,when executed by one or more processing modules of one or more computingdevices (e.g., one or more servers, one or more user devices) of thecomputing system 10, cause the one or more computing devices to performany or all of the method steps described above.

FIG. 15A is a schematic block diagram of another embodiment of acomputing system that includes relationship rich content sources 820,the AI server 20-1 of FIG. 1, and the user device 12-1 of FIG. 1. Therelationship rich content sources 820 includes the content sources 16-1through 16-N of FIG. 1 and provides content that exposes relationships(e.g., main actors, victims, verbs, things, etc.) in addition to facts.The AI server 20-1 includes the processing module 50-1 of FIG. 2 and theSS memory 96 of FIG. 2. The processing module 50-1 includes thecollections module 120 of FIG. 4A, the IEI module 122 of FIG. 4A, andthe query module 124 of FIG. 4A. The SS memory 96 includes a primaryknowledge database 822 and a relationships database 824. Alternatively,a single database may provide common storage facilities. Generally, anembodiment of the invention presents solutions where the computingsystem functions to extract relationship information from contact.

In an example of operation of the extracting of the relationshipinformation, when analyzing ingested content to produce knowledge, theIEI module 122 determines possible meanings of a phrase of the content.For example, the IEI module 122 issues a collections request 132 to thecollections module 120 and receives a collection response 134 thatincludes the content, where the collections module 120 issues contentrequests 126 to the content sources 16-1 through 16-N of therelationship rich content sources 820 and receives content responses 128that includes the content. The IEI module 122 applies IEI processing thecontent of the collections response 134 to identify elements inaccordance with element rules and the phrase that includes identifiedelements in accordance with phrase identification rules, and matchesidentified phrases with corresponding meanings in accordance with rulesand a phrase list to produce the possible meanings of the phrase.

Having determined the possible meanings, the IEI module 122 selects atleast one meaning of the possible meanings as an interpreted meaning toproduce facts 826 for storage in the primary knowledge database 822. Forexample, the IEI module 122 scores each of the possible meanings (e.g.,past successful interpretations, graphical database comparison, by risklevel, etc.) and selects based on the score (e.g., high score associatedwith the one meaning).

Having selected the at least one meaning, the IEI module 122 generatesrelationship information 828 for storage in the relationships database824, where the relationship information 828 is based on the possiblemeanings of each phrase. For example, the IEI module 122 generates therelationship information 828 to include one or more of detected portionsof phrases and associated possible meanings, frequency of occurrence ofeach detected possible meaning, inferred relationship between two ormore entities based on possible meanings, final selected meanings,relationship rules, and relationship history. The IEI module 122 mayfurther store detected portions of phrases and possible meaningsincluding the selected meaning for each phrase to enable subsequentaccess of the relationship information 828 for aggregation to producefrequency of occurrence of each detected possible meaning and to developinferred relationships between two or more entities based on thepossible meanings and relationship history.

Having generated the relationship information 828, when responding to aquestion associated with a relationship, the IEI module 122 accesses therelationship information 828 to issue a corresponding answer. Forexample, the IEI module 122 receives an IEI request 244 from the querymodule 124, where the query module 124 generates the IEI request 244based on receiving a query request 136 that includes the question fromthe user device 12-1, identifies a domain and relationship nature of thequestion in accordance with question rules and/or relationship rules.When identifying that the question is associated with the relationship,the IEI module 122 accesses a portion of the relationships database 824to obtain relationship information 828 relevant to the question. Whengenerating the question, the IEI module 122 issues and IEI response 246that includes an answer to the query module 124, where the query module124 issues a query response 140 to the user device 12-1.

FIG. 15B is a logic diagram of an embodiment of a method for extractingrelationship information from content within a computing system. Inparticular, a method is presented for use in conjunction with one ormore functions and features described in conjunction with FIGS. 1-8D,15A, and also FIG. 15B. When analyzing adjusted content to produceknowledge, the method includes step 840 where a processing module of oneor more processing modules of one or more computing devices of thecomputing system determines possible meanings of a phrase of thecontent. For example, the processing module issues a collections requestto a collections module and receives a collections response thatincludes the content, where the collections module issues contentrequests to one or more content sources and receives content responsesthat includes the content. Having acquired the content, the processingmodule applies JET processing to the received content to identifyelements in accordance with elements rules and the phrase that includesidentified elements in accordance with phrase identification rules,matches identified phrases with corresponding meetings in accordancewith the rules and a phrase list to produce the possible meanings ofphrase.

The method continues at step 842 where the processing module selects atleast one meaning of the possible meanings as an interpreted meaning toproduce facts for storage in a primary knowledge database. For example,the processing module scores (e.g., based on one or more of pastsuccessful interpretations, a graphical database comparison, risklevels, etc.) each of the possible meanings and selects the one meaningbased on the score (e.g., a highest compatibility score).

The method continues at step 844 where the processing module generatesrelationship information for storage in a relationship database, wherethe relationship information is based on the possible meanings of thephrase. For example, the processing module stores detected portions ofphrases and possible meanings so that the selected meanings for eachphrase can be subsequently accessed along with relationship informationfrom the relationships database to enable aggregation to producefrequency of occurrence of each detected possible meaning and inferredrelationships between two or more entities based on the possiblemeanings and further relationship history.

When responding to a question associated with a relationship, the methodcontinues at step 846 for the processing module accesses therelationship information to issue a corresponding answer. For example,the processing module receives the question from a requesting entity,identifies a domain and relationship nature of the question inaccordance with the question rules and/or relationship rules and whenidentifying that the question is associated with a relationship,accesses a portion of the relationships database to obtain relationshipinformation relevant to the question, produces the answer based on therelationship information, and outputs the answer to the requestingentity.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIGS. 16A, 16C, 16E, and 16G are schematic block diagram of anotherembodiment of a computing system illustrating an example of a method forupdating a document utilizing trusted new information. The computingsystem includes IEI modules 122-1 and 122-2, a meaning comparison module850, an update module 852, and the knowledge database 800 of FIG. 14A.Each of the IEI modules 122-1 and 122-2 may be implemented utilizing theIEI module 122 of FIG. 4A. Each of the meaning comparison module 850 andthe update module 852 may be implemented utilizing one or more of theprocessing modules 50-1 through 50-N of FIG. 2.

FIG. 16A illustrates the example of the method for updating the documentutilizing the trusted new information where the IEI module 122-1generates a new entigen group 856 regarding the trusted new information854. The new entigen group represents a most likely meaning of thetrusted new information. For example, the IEI module 122-1 receives thetrusted new information 854 that includes a string of words thatrepresents curated reliable knowledge (e.g., factual, to be taken atface value). The IEI module 122-1, for each word of the string of words,retrieves a set of word identigens from the knowledge database 800 toproduce a plurality of sets of identigens. The IEI module 122-1 appliesidentigen sequencing rules to the plurality of sets of identigens toproduce the new entigen group 856.

The entigen group includes entigens corresponding to the string of wordsand relationships between the entigens. A graphical representation ofthe entigen group includes circles for the entigens and connectorsbetween the circles to describe the relationships between the entigens.

FIG. 16B further illustrates the example of the method for updating thedocument utilizing the trusted new information where the new entigengroup 856 is generated based on an instance string of words of thetrusted information 854 that includes “black and brown bats found inTexas and Mexico inhabit New Mexico”. The instance of the new entigengroup 856 includes black and brown characteristic entigens coupled to abat object entigen. The bat object entigen is coupled to an inhabitsaction entigen which is coupled to recipient object entigens New Mexico,Mexico, and Texas.

FIG. 16C further illustrates the example of the method for updating thedocument utilizing the trusted new information where the IEI module122-2 generates a plurality of entigen groups 860 from a plurality ofphrases 858 of a document. The plurality of entigen groups represents aplurality of most likely meanings for the plurality of phrases. Forexample, the IEI module 122-2 receives the plurality of phrases 858 fromone or more documents (e.g., to be updated), and for each word of thephrases, retrieves a set of word identigens from the knowledge database800 to produce another plurality of sets of identigens. The IEI module122-2 applies the identigen sequencing rules to the other plurality ofsets of identigens to produce each entigen group of the plurality ofentigen groups 860.

As a further example, in a similar fashion, the IEI module 122-2generates a second plurality of entigen groups from a second pluralityof phrases of a second document. The second plurality of entigen groupsrepresents a plurality of most likely meanings for the second pluralityof phrases.

FIG. 16D further illustrates the example of the method for updating thedocument utilizing the trusted new information where a first entigengroup is generated from a first instance phrase of “brown bats of Mexicoeat insects”, where the first entigen group includes a browncharacteristic entigen coupled to a bat object entigen. The bat objectentigen is coupled to an action inhabits entigen which is coupled to aMexico recipient characteristic entigen. The bat object entigen isfurther coupled to an eats action entigen which is coupled to arecipient insects object entigen.

A second entigen group is generated from a second instance phrase of“black bats of Texas eat fruit”, where the second entigen group includesa black characteristic entigen coupled to a bat object entigen. The batobject entigen is coupled to an inhabits action entigen which is coupledto a Texas recipient object entigen. The bat object entigen is furthercoupled to an eats action entigen which is coupled to a fruit recipientobject entigen. An embodiment, the second entigen group is associatedwith the plurality of entigen groups. In another embodiment, the secondentigen group is associated with the second plurality of entigen groups.

FIG. 16E further illustrates the example of the method for updating thedocument utilizing the trusted new information where the meaningcomparison module 850 determines whether an entigen group of theplurality of entigen groups has a most likely meaning similar to themost likely meaning of the new entigen group. In a similar fashion, themeaning comparison module 850 determines whether a second entigen groupof the second plurality of entigen groups has a most likely meaningsimilar to the most likely meaning of the new entigen group. The meaningcomparison module 850 outputs similar entigen group(s) 862 to includethe entigen group that has the most likely similar meaning.

The determining whether the entigen group and the second entigen grouphas a most likely meaning similar to the most likely meaning of the newentigen group further includes several approaches. A first approachincludes comparing a subset of entigens of the new entigen group to aportion of the plurality of entigen groups and the second plurality ofentigen groups. For example, the meaning comparison module 850 searchesthrough substantially all portions of the plurality of entigen groups tolocate an entigen group that includes an entigen that substantiallymatches an entigen of the new entigen group.

When the subset of entigens of the new entigen group compares favorably(e.g., substantially matches) to at least one entigen group of theplurality of entigen groups and the second plurality of entigen groups,the meaning comparison module 850 identifies the at least one entigengroup as having a most likely meaning similar to the most likely meaningof the new entigen group. An instance of an example is discussed ingreater detail with reference to FIG. 16F.

A second approach to the determining whether the entigen group and thesecond entigen group has a most likely meaning similar to the mostlikely meaning of the new entigen group includes identifying the portionof the plurality of entigen groups and the second plurality of entigengroups based on the new entigen group and utilizing an index entigengroup. The index entigen group includes a plurality of index entigens ofconnected meaning. A linking index entigen of the index entigen groupidentifies the portion of the plurality of entigen groups and the secondplurality of entigen groups. The linking index entigen comparesfavorably to the new entigen group. An instance of an example utilizingthe index entigen group is discussed in greater detail with reference toFIG. 16F.

FIG. 16F further illustrates the example of the method for updating thedocument utilizing the trusted new information where the entigen groups1 and 2 are identified to have the most likely meaning similar to themost likely meaning of the new entigen group since they all relate tobats. In particular, they each relate to bats inhabiting variousgeographies.

When utilizing the index entigen group, entigens of the index entigengroup are coupled to the entigens of the plurality of entigen groups.For example, entigens describing what bats eat are linked, entigensdescribing colors of bats are linked, and entigens describing where batsinhabit are linked.

FIG. 16G further illustrates the example of the method for updating thedocument utilizing the trusted new information where, when the entigengroup has a most likely meaning similar to the most likely meaning ofthe new entigen group, the update module 852 updates the entigen groupbased on the new entigen group to produce updated plurality of entigengroups 864. For example, the update module 852 adds an amending entigenwhen the new information includes new knowledge for a topic that is atleast partially included in the information of the document.

As another example of the updating the entigen group based on the newentigen group, the update module 852 identifies a conflicting entigen ofthe entigen group that conflicts with a correcting entigen of the newentigen group, where the conflicting entigen and the correcting entigenare associated with a common entigen type (e.g., different meaning forwhat bats eat). Having identified the conflicting entigen, the updatemodule 852 replaces the conflicting entigen of the entigen group withthe correcting entigen of the new entigen group. In a similar fashion,the update module 852, when the second entigen group has a most likelymeaning similar to the most likely meaning of the new entigen group, theupdate module 852 updates the second entigen group based on the newentigen group.

When the plurality of entigen groups does not include an entigen grouphaving a most likely meaning similar to the most likely meaning of thenew entigen group, the update module 852 updates the plurality ofentigen groups to include the new entigen group. For example, the updatemodule 852 adds the amending entigen when nothing similar is included inthe information of the document. In a similar fashion, the update module852, when the second plurality of entigen groups does not include anentigen group having a most likely meaning similar to the most likelymeaning of the new entigen group, updates the second plurality ofentigen groups to include the new entigen group. Example instances ofapplying the amending entigen to the entigen groups is discussed ingreater detail with reference to FIG. 16H.

FIG. 16H further illustrates the example of the method for updating thedocument utilizing the trusted new information where the updating theentigen group based on the new entigen group includes several steps. Afirst step includes identifying an amending entigen 868 (e.g., newknowledge) of the new entigen group, where the entigen group does notinclude the amending entigen 868. For instance, the New Mexico objectentigen is identified as the amending entigen 868 when entigen groups 1and 2 do that include the New Mexico object entigen.

A second step of updating the entigen group based on the new entigengroup includes identifying a connective entigen 866 that is included inboth of the entigen group and the new entigen group, where theconnective entigen 866 of the new entigen group is associated with theamending entigen 868. For instance, the inhabits action entigen isidentified as the connective entigen 866 as it is coupled to the NewMexico object amending entigen 868 of the new entigen group 856

A third step of updating the entigen group based on the new entigengroup includes updating the entigen group to include the amendingentigen 868. For instance, the index entigen group is utilized to locatethe connective entigen 866 (e.g., inhabits action entigen) of theentigen groups 1 and 2 by following the link from the inhabits entigenof the index entigen group. Having located the connective entigens 866,the amending entigen 868 (e.g., New Mexico) is coupled to the connectiveentigens 866 of the entigen groups 1 and 2.

A fourth step of updating the entigen group based on the new entigengroup includes associating the amending entigen of the entigen groupwith the connective entigen of the entigen group. For instance, theconnective entigens 866 are coupled to the amending entigens 868 torepresent the relationship, e.g., inhabiting, does to, New Mexico.

The method described above can alternatively be performed by othermodules of the computing system 10 of FIG. 1 or by other devices. Inaddition, at least one memory section (e.g., a computer readable memory,a non-transitory computer readable storage medium, a non-transitorycomputer readable memory organized into a first memory element, a secondmemory element, a third memory element, a fourth element section, afifth memory element etc.) that stores operational instructions can,when executed by one or more processing modules of one or more computingdevices (e.g., one or more servers, one or more user devices) of thecomputing system 10, cause the one or more computing devices to performany or all of the method steps described above.

FIG. 17A is a schematic block diagram of another embodiment of anotherembodiment of a computing system that includes document content sources870, the AI server 20-1 of FIG. 1, and the user device 12-1 of FIG. 1.The document content sources 870 includes the content sources 16-1through 16-N of FIG. 1 and provides content associated with a documentto be verified for accuracy as described below. The AI server 20-1includes the processing module 50-1 of FIG. 2 and the SS memory 96 ofFIG. 2. The processing module 50-1 includes the collections module 120of FIG. 4A, the IEI module 122 of FIG. 4A, and the query module 124 ofFIG. 4A. Generally, an embodiment of the invention presents solutionswhere the computing system functions to substantiate accuracy of adocument.

In an example of operation of the substantiating the accuracy of thedocument, the IEI module 122 analyzes content of the document to producedocument knowledge. For example, the IEI module 122 receives, from thequery module 124, an IEI request 244, where the query module 124generates the IEI request 244 based on receiving a document verificationrequest 872 from the user device 12-1, where the document verificationrequest 872 includes one or more of the document for verification,document retrieval information (e.g., when the document is notreceived), and document metadata (e.g., domain, topic, author,identifiers of content sources, other content, etc.).

The IEI module 122 obtains the document (e.g., extract from the documentverification request 872 and/or retrieve the document based on thedocument retrieval information). Having obtained the document, the IEImodule 122 applies IEI processing to the document to identify elementsin accordance with element rules, matches identified elements withcorresponding meanings in accordance with the rules to produce thedocument knowledge, and, in addition may aggregate the documentknowledge with further knowledge extracted from the fact-baseinformation 600 retrieved from the SS memory 96, where the fact-basedinformation 600 is associated with the document knowledge (e.g., sameauthor, knowledge from another author known to create documents relatedto the document, same domain, same topic, etc.).

Having produced the document knowledge, the IEI module 122 identifiescontent of the document content sources 870 associated with thedocument. The identified includes one or more of interpreting a query,extracting content identifiers from the list, and extracting the contentidentity from document metadata.

Having identified the content, the IEI module 122 transforms theidentified content of the document content sources 870 into re-createddocument knowledge. As an example of the transforming, the IEI module122 issues a collections request 132 to the collections module 120 andreceives a collections response 134 that includes the identifiedcontent, where the collections module 120 issues content requests 126 tothe content sources 16-1 through 16-N of the document content sources870 and receives content responses 128 that includes the identifiedcontent. As another example of the transforming, the IEI module 122extracts content from IEI request 244, where the query module 124receives a document verification request 872 and/or a query request fromthe user device 12-1, extracts content from the request, and issues theIEI request 244 to the IEI module 122. The IEI module 122 applies IEIprocessing to the received content to identify elements in accordancewith element rules, matches identified elements with correspondingmeanings in accordance with the rules to produce the re-created documentknowledge.

Having produced the re-created document knowledge, the IEI module 122indicates a level of verification accuracy of the received documentbased on a comparison of the re-created document knowledge with thedocument knowledge. The indicating includes one or more of indicating afavorable level of verification accuracy of the received document whenthe comparison is favorable (e.g., confirming knowledge), and indicatesin unfavorable level of the verification accuracy of the receiveddocument when the comparison is unfavorable (e.g., conflictingknowledge). In addition, the IEI module 122 may issue an IEI response246 to the query module 124, where the query module 124 issues adocument verification response 874 to the user device 12-1, where thedocument verification response 874 includes the level of verificationaccuracy.

FIG. 17B is a logic diagram of an embodiment of a method forsubstantiating accuracy of a document within a computing system. Inparticular, a method is presented for use in conjunction with one ormore functions and features described in conjunction with FIGS. 1-8D,17A, and also FIG. 17B. When verifying the accuracy of the document, themethod includes step 880 where a processing module of one or moreprocessing modules of one or more computing devices of the computingsystem analyzes the document to produce document knowledge. For example,the processing module obtains the document (e.g., extracts from arequest, retrieves from a document storage facility), applies IEIprocessing to the document to identify elements in accordance withelements rules, matches identified elements with corresponding meaningsin accordance with the rules to produce document knowledge, and inaddition, may aggregate the document knowledge with further knowledgeextracted from the knowledge base, where the further knowledge isassociated with the document knowledge (e.g., same author, knowledgefrom another author known to create documents related to the document,same domain, same topic, etc.).

The method continues at step 882 where the processing module identifiesexternal content associated with the document. For example, theprocessing module interprets a query, extracts content identifiers froma list, and extracts identifiers and/or content from document metadata.The method continues at step 884 where the processing module transformsat least some of the external content to produce re-created documentknowledge. As an example of the transforming, the processing moduleobtains the external content from one or more content sources, and/orextracts content from request, applies JET processing to the obtainedexternal content to identify elements in accordance with element rules,and matches the identified elements with corresponding meanings inaccordance with the rules to produce the re-created document knowledge.

The method continues at step 886 where the processing module indicates alevel of verification accuracy of the document base in an example of theindicating, the processing module indicates a favorable level ofverification accuracy of the received document when the comparison isfavorable (e.g., confirming knowledge, indicates an unfavorable level ofverification accuracy of the received document when the comparison isunfavorable (e.g., conflicting knowledge). The processing module mayalso issue a response to an entity requesting the verification of theaccuracy of the document, the response includes the level ofverification accuracy based on a comparison of the re-created documentknowledge with the document knowledge.

Alternatively, or in addition to, the processing module may compare thedocument knowledge created by a first processing module to re-createddocument knowledge created by second processing module to produce thelevel of verification accuracy. Further, the processing module maycompare the document knowledge created by the first processing module tothe re-created document knowledge created by the second processingmodule to produce a level of verification accuracy, where the secondprocessing module applies JET processing to the document (e.g., ratherthan to the content) to produce the re-created document knowledge (e.g.,this serves more to verify accurate knowledge creation from the documentby the first processing module).

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 18A is a schematic block diagram of another embodiment of anotherembodiment of a computing system that includes the elementidentification module 302 of FIG. 5E and the interpretation module 304of FIG. 5E. The element identification module 302 includes the elementmatching module 400 of FIG. 6A, the element grouping module 402 of FIG.6A, and an optimization module 890. The interpretation module 304includes the grouping matching module 404 of FIG. 6A and the groupinginterpretation module 406 of FIG. 6A. Generally, an embodiment of theinvention presents solutions where the computing system functions tointerpret a phrase to produce a meaning when processing the phrase toproduce knowledge.

In an example of operation of the interpreting of the phrase, whenadjusting the phrase for the knowledge extraction, the optimizationmodule 890 generates analysis equations 892 to be utilized in asubsequent analysis of the phrase. The analysis equations 892 includesequations (e.g., algebraic/numerical constructs, graphical constructs,logical constructs) utilized in the subsequent phrase analysis, wherethe equations are selected and/or generated based on one or more of adetected domain, a particular source of content, historical solutionsand outcomes, user input, a detected language, a detected dialect,context of utilization of the phrase, etc. For example, the optimizationmodule 890 accesses one or more of an element list 332, element rules318, a groupings list 334, and interpretation rules 320 to identify theequations.

Having produced the analysis equations 892, the optimization module 890generates equation parameters 894 (e.g., constants, ranges, etc.) forutilization within the analysis equations 892. The generating may bebased on the analysis equations 892 where the optimization module 890accesses one or more of the element list 332, the element rules 318,groupings list 334, and the interpretation rules 320 to identifycandidate equation parameters. For each selected equation of theanalysis equations 892, the optimization module 890 further selectsequation parameters from the candidate equation parameters based on oneor more of the phrase, a mapping of the phrase to ranges of parameters,parameter types associated with previously favorable knowledgeextraction, a detected domain, a request, etc.

The element matching module 400 generates matched elements 412 forelements of the phrase based on one or more of the element list 332, theanalysis equations 892, and the equation parameters 894. For example,the element matching module 400 matches words of the phrase to words ofthe element list 332, when the matching compares favorably to a selectedanalysis equation when utilizing associated equation parameters. Theelement grouping module 402 generates identified element information 340corresponding to the matched elements 412 based on one or more of theelement list 332, the element rules 318, the analysis equations 892, andthe equation parameters 894. For example, the element grouping module402 matches a word group of the phrase to a phrase portion of theelement list 332 in accordance with the element rules 318 to produce theidentified element information 340, when the matching compares favorablyto a selected analysis equation when utilizing associated equationparameters.

The grouping matching module 404 processes the identified elementinformation 340 based on one or more of the groupings list 334, theanalysis equations 892, and the equation parameters 894 to producevalidated groupings information 416. For example, the grouping matchingmodule 404 matches the identified element information 340 of the phraseto a plurality of potential valid groupings of the groupings list 334 toproduce the validated groupings information 416, when the matchingcompares favorably to a selected analysis equation when utilizingassociated equation parameters.

The grouping interpretation module 406 processes the validated groupingsinformation 416 based on one or more of interpretation rules 320,question information 346, the analysis equations 892, and the equationparameters 894 to produce interpreted information 344 (e.g., most likelyinterpretations, next likely interpretations, etc.). For example, thegrouping interpretation module 406, based on a plurality of possiblemeetings allowed by the interpretation rules 320 and in accordance withthe question information 346, prunes (e.g., eliminates least likelymeanings) the plurality of possible meanings based on utilizing theequation parameters 894 within the analysis equations 892 (e.g.,eliminating a meaning when a quality level of the equation isunfavorable) to produce one or more most likely meanings and outputs theone or more likely meanings as an interpretation of the phrase (e.g.,interpreted information 344).

FIG. 18B is a logic diagram of an embodiment of a method forinterpreting a phrase within a computing system. In particular, a methodis presented for use in conjunction with one or more functions andfeatures described in conjunction with FIGS. 1-8D, 18A, and also FIG.18B. When ingesting a phrase for knowledge extraction, the methodincludes step 900 where a processing module of one or more processingmodules of one or more computing devices of the computing systemgenerates analysis equations to be utilized in a subsequent analysis ofthe phrase. For example, the processing module accesses one or more ofan element list, element rules, a groupings list, and interpretationrules to identify candidate equations, and, for each selected equation,selects an equation from the candidate equations based on one or more ofa detected domain, a source, historical selections and outcomes, userinput, a detected language, and a detected dialect.

The method continues at step 902 where the processing module generatesequation parameters to be utilized in conjunction with the analysisequations in the subsequent analysis the phrase. For example, theprocessing module, based on the analysis equations, accesses one or moreof the element list, the element rules, the groupings list, and theinterpretation rules to identify candidate equation parameters, and, foreach selected equation, selects equation parameters from the candidateequation parameters based on one or more of the phrase, a mapping of thephrase to ranges of parameters, parameters associated with a favorableknowledge extraction, a detected domain, etc.

The method continues at step 904 where the processing module generatesmatched elements for the phrase. For example, the processing modulematches words of the phrase to words of an elements list based on one ormore of the analysis equations and the equation parameters to producethe matched elements.

The method continues at step 906 where the processing module processesthe matched elements to produce validated groupings information. Forexample, the processing module generates identified element informationcorresponding to the matched elements based on one or more of theelement list, element rules, the analysis equations, and the equationparameters (e.g., matching word group of the phrase to a phrase portionof the element list). The processing module processes the identifiedelement information utilizing a groupings list and based on one or moreof the analysis equations and the equation parameters to produce thevalidated groupings information (e.g., match the identified elementinformation of the phrase to a plurality of potential valid groupings ofthe groupings list).

The method continues at step 908 where the processing module processesthe validated groupings information utilizing the equation parameterswithin the analysis equations to produce interpreted information. Forexample, the processing module, based on the plurality of possiblemeetings allowed by the interpretation rules and in accordance withquestion information, reduces the plurality of possible meanings basedon utilizing the equation parameters within the analysis equations(e.g., eliminates a particular meaning when a quality level of anequation associated with the meaning is unfavorable), and outputs ameaning that survives the reducing as the interpreted information.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a computing device, themethod comprises: obtaining content for a topic that includes aplurality of words; determining a set of identigens for each word of theplurality of words of the content to produce a plurality of sets ofidentigens, wherein each identigen of the set of identigens includes ameaning identifier, an instance identifier, and a time reference,wherein each meaning identifier associated with a particular set ofidentigens represents a different meaning of one or more differentmeanings of a corresponding word of the plurality of words of thecontent, wherein each time reference provides time information when acorresponding different meaning of the one or more different meanings isvalid, wherein a first set of identigens of the plurality of sets ofidentigens is produced for a first word of the plurality of words of thecontent; interpreting, in accordance with identigen pairing rules of afirst knowledge database, the plurality of sets of identigens todetermine a most likely meaning interpretation of the content andproduce a baseline entigen group comprising a plurality of baselineentigens for storage in the first knowledge database, wherein the firstknowledge database includes a first multitude of entigen groupsassociated with a first multitude of topics, wherein the first multitudeof topics includes the topic, wherein each entigen group of the firstmultitude of entigen groups includes a corresponding plurality ofentigens and one or more entigen relationships between at least some ofthe corresponding plurality of entigens, wherein the baseline entigengroup represents the most likely meaning interpretation of the content,wherein each baseline entigen of the baseline entigen group correspondsto a selected identigen of the set of identigens having a selectedmeaning of the one or more different meanings of each word of theplurality of words, wherein each baseline entigen of the baselineentigen group represents a single conceivable and perceivable thing inspace and time that is independent of language and corresponds to a timereference of the selected identigen associated with the baseline entigengroup, wherein the selected identigen favorably pairs with at least onecorresponding sequentially adjacent identigen of another set ofidentigens of the plurality of sets of identigens based on the identigenpairing rules of the first knowledge database; recovering an incompleteentigen group for the topic from the first knowledge database based on aknowledge defect of the incomplete entigen group with regards to thetopic, wherein the incomplete entigen group includes at least some ofthe plurality of baseline entigens of the baseline entigen group,wherein the incomplete entigen group includes a plurality of incompleteentigens and one or more entigen relationships between at least some ofthe plurality of incomplete entigens, wherein the incomplete entigengroup represents at least some knowledge of the topic; obtaining anadditive entigen group from a second knowledge database based on theknowledge defect of the incomplete entigen group with regards to thetopic, wherein the second knowledge database includes a second multitudeof entigen groups associated with a second multitude of topics, whereinthe second multitude of topics includes the topic, wherein each entigengroup of the second multitude of entigen groups includes a correspondingplurality of entigens of the second multitude of entigen groups and oneor more entigen relationships between at least some of the correspondingplurality of entigens of the second multitude of entigen groups; andmodifying the incomplete entigen group utilizing the additive entigengroup to produce an updated entigen group to provide a beneficial curefor the knowledge defect of the incomplete entigen group.
 2. The methodof claim 1 further comprises one or more of: storing the updated entigengroup in the first knowledge database to replace the incomplete entigengroup; storing the updated entigen group in the second knowledgedatabase to replace the additive entigen group; and outputting, via auser interface of the computing device, a representation of the updatedentigen group with an indication of a curated status.
 3. The method ofclaim 1 further comprises: determining the knowledge defect of theincomplete entigen group with regards to the topic by one or more of:determining that a number of entigens of the incomplete entigen group isless than a minimum number of entigens threshold number; determiningthat the incomplete entigen group does not contain an expected yetmissing entigen of an expected category; and determining that theincomplete entigen group does not contain an expected yet missingentigen relationship between first and second entigens of the incompleteentigen group.
 4. The method of claim 1, wherein the recovering theincomplete entigen group for the topic from the first knowledge databasebased on the knowledge defect of the incomplete entigen group withregards to the topic comprises: obtaining a subject entigen group fromthe first knowledge database that includes at least some of theplurality of baseline entigens of the baseline entigen group;identifying a knowledge defect of the subject entigen group; andestablishing the subject entigen group as the incomplete entigen groupand the knowledge defect of the subject entigen group as the knowledgedefect of the incomplete entigen group.
 5. The method of claim 1,wherein the obtaining the additive entigen group from the secondknowledge database based on the knowledge defect of the incompleteentigen group with regards to the topic comprises: identifying at leastone of a missing entigen and a missing entigen relationship of theknowledge defect of the incomplete entigen group; obtaining a candidateentigen group from the second knowledge database that includes at leastsome of the plurality of incomplete entigens of the incomplete entigengroup; determining that the candidate entigen group further includes asolution for the at least one of the missing entigen and the missingentigen relationship of the knowledge defect of the incomplete entigengroup; and establishing the candidate entigen group as the additiveentigen group.
 6. The method of claim 1, wherein the modifying theincomplete entigen group utilizing the additive entigen group to producethe updated entigen group comprises: identifying at least one of amissing entigen and a missing entigen relationship of the knowledgedefect of the incomplete entigen group; extracting a solution for the atleast one of the missing entigen and the missing entigen relationship ofthe knowledge defect of the incomplete entigen group from the additiveentigen group; and supplementing the incomplete entigen group with thesolution to produce the updated entigen group.
 7. A computing device ofa computing system, the computing device comprises: an interface; a userinterface; a local memory; and a processing module operably coupled tothe interface, the user interface, and the local memory, wherein theprocessing module functions to: obtain, via the interface, content for atopic that includes a plurality of words; determine a set of identigensfor each word of the plurality of words of the content to produce aplurality of sets of identigens, wherein each identigen of the set ofidentigens includes a meaning identifier, an instance identifier, and atime reference, wherein each meaning identifier associated with aparticular set of identigens represents a different meaning of one ormore different meanings of a corresponding word of the plurality ofwords of the content, wherein each time reference provides timeinformation when a corresponding different meaning of the one or moredifferent meanings is valid, wherein a first set of identigens of theplurality of sets of identigens is produced for a first word of theplurality of words of the content; interpret, in accordance withidentigen pairing rules of a first knowledge database, the plurality ofsets of identigens to determine a most likely meaning interpretation ofthe content and produce a baseline entigen group comprising a pluralityof baseline entigens for storage in the first knowledge database,wherein the first knowledge database includes a first multitude ofentigen groups associated with a first multitude of topics, wherein thefirst multitude of topics includes the topic, wherein each entigen groupof the first multitude of entigen groups includes a correspondingplurality of entigens and one or more entigen relationships between atleast some of the corresponding plurality of entigens, wherein thebaseline entigen group represents the most likely meaning interpretationof the content, wherein each baseline entigen of the baseline entigengroup corresponds to a selected identigen of the set of identigenshaving a selected meaning of the one or more different meanings of eachword of the plurality of words, wherein each baseline entigen of thebaseline entigen group represents a single conceivable and perceivablething in space and time that is independent of language and correspondsto a time reference of the selected identigen associated with thebaseline entigen group, wherein the selected identigen favorably pairswith at least one corresponding sequentially adjacent identigen ofanother set of identigens of the plurality of sets of identigens basedon the identigen pairing rules of the first knowledge database; recover,via the interface, an incomplete entigen group for the topic from thefirst knowledge database based on a knowledge defect of the incompleteentigen group with regards to the topic, wherein the incomplete entigengroup includes at least some of the plurality of baseline entigens ofthe baseline entigen group, wherein the incomplete entigen groupincludes a plurality of incomplete entigens and one or more entigenrelationships between at least some of the plurality of incompleteentigens, wherein the incomplete entigen group represents at least someknowledge of the topic; obtain, via the interface, an additive entigengroup from a second knowledge database based on the knowledge defect ofthe incomplete entigen group with regards to the topic, wherein thesecond knowledge database includes a second multitude of entigen groupsassociated with a second multitude of topics, wherein the secondmultitude of topics includes the topic, wherein each entigen group ofthe second multitude of entigen groups includes a correspondingplurality of entigens of the second multitude of entigen groups and oneor more entigen relationships between at least some of the correspondingplurality of entigens of the second multitude of entigen groups; andmodify the incomplete entigen group utilizing the additive entigen groupto produce an updated entigen group to provide a beneficial cure for theknowledge defect of the incomplete entigen group.
 8. The computingdevice of claim 7, wherein the processing module further functions to:store, via the interface, the updated entigen group in the firstknowledge database to replace the incomplete entigen group; store, viathe interface, the updated entigen group in the second knowledgedatabase to replace the additive entigen group; and output, via theinterface, a representation of the updated entigen group with anindication of a curated status.
 9. The computing device of claim 7,wherein the processing module further functions to: determine theknowledge defect of the incomplete entigen group with regards to thetopic by one or more of: determining that a number of entigens of theincomplete entigen group is less than a minimum number of entigensthreshold number; determining that the incomplete entigen group does notcontain an expected yet missing entigen of an expected category; anddetermining that the incomplete entigen group does not contain anexpected yet missing entigen relationship between first and secondentigens of the incomplete entigen group.
 10. The computing device ofclaim 7, wherein the processing module functions to recover theincomplete entigen group for the topic from the first knowledge databasebased on the knowledge defect of the incomplete entigen group withregards to the topic by: obtaining, via the interface, a subject entigengroup from the first knowledge database that includes at least some ofthe plurality of baseline entigens of the baseline entigen group;identifying a knowledge defect of the subject entigen group; andestablishing the subject entigen group as the incomplete entigen groupand the knowledge defect of the subject entigen group as the knowledgedefect of the incomplete entigen group.
 11. The computing device ofclaim 7, wherein the processing module functions to obtain the additiveentigen group from the second knowledge database based on the knowledgedefect of the incomplete entigen group with regards to the topic by:identifying at least one of a missing entigen and a missing entigenrelationship of the knowledge defect of the incomplete entigen group;obtaining, via the interface, a candidate entigen group from the secondknowledge database that includes at least some of the plurality ofincomplete entigens of the incomplete entigen group; determining thatthe candidate entigen group further includes a solution for the at leastone of the missing entigen and the missing entigen relationship of theknowledge defect of the incomplete entigen group; and establishing thecandidate entigen group as the additive entigen group.
 12. The computingdevice of claim 7, wherein the processing module functions to modify theincomplete entigen group utilizing the additive entigen group to producethe updated entigen group by: identifying at least one of a missingentigen and a missing entigen relationship of the knowledge defect ofthe incomplete entigen group; extracting a solution for the at least oneof the missing entigen and the missing entigen relationship of theknowledge defect of the incomplete entigen group from the additiveentigen group; and supplementing the incomplete entigen group with thesolution to produce the updated entigen group.
 13. A computer readablememory comprises: a first memory element that stores operationalinstructions that, when executed by a processing module, causes theprocessing module to: obtain content for a topic that includes aplurality of words, and determine a set of identigens for each word ofthe plurality of words of the content to produce a plurality of sets ofidentigens, wherein each identigen of the set of identigens includes ameaning identifier, an instance identifier, and a time reference,wherein each meaning identifier associated with a particular set ofidentigens represents a different meaning of one or more differentmeanings of a corresponding word of the plurality of words of thecontent, wherein each time reference provides time information when acorresponding different meaning of the one or more different meanings isvalid, wherein a first set of identigens of the plurality of sets ofidentigens is produced for a first word of the plurality of words of thecontent; a second memory element that stores operational instructionsthat, when executed by the processing module, causes the processingmodule to: interpret, in accordance with identigen pairing rules of afirst knowledge database, the plurality of sets of identigens todetermine a most likely meaning interpretation of the content andproduce a baseline entigen group comprising a plurality of baselineentigens for storage in the first knowledge database, wherein the firstknowledge database includes a first multitude of entigen groupsassociated with a first multitude of topics, wherein the first multitudeof topics includes the topic, wherein each entigen group of the firstmultitude of entigen groups includes a corresponding plurality ofentigens and one or more entigen relationships between at least some ofthe corresponding plurality of entigens, wherein the baseline entigengroup represents the most likely meaning interpretation of the content,wherein each baseline entigen of the baseline entigen group correspondsto a selected identigen of the set of identigens having a selectedmeaning of the one or more different meanings of each word of theplurality of words, wherein each baseline entigen of the baselineentigen group represents a single conceivable and perceivable thing inspace and time that is independent of language and corresponds to a timereference of the selected identigen associated with the baseline entigengroup, wherein the selected identigen favorably pairs with at least onecorresponding sequentially adjacent identigen of another set ofidentigens of the plurality of sets of identigens based on the identigenpairing rules of the first knowledge database; a third memory elementthat stores operational instructions that, when executed by theprocessing module, causes the processing module to: recover anincomplete entigen group for the topic from the first knowledge databasebased on a knowledge defect of the incomplete entigen group with regardsto the topic, wherein the incomplete entigen group includes at leastsome of the plurality of baseline entigens of the baseline entigengroup, wherein the incomplete entigen group includes a plurality ofincomplete entigens and one or more entigen relationships between atleast some of the plurality of incomplete entigens, wherein theincomplete entigen group represents at least some knowledge of thetopic; a fourth memory element that stores operational instructionsthat, when executed by the processing module, causes the processingmodule to: obtain an additive entigen group from a second knowledgedatabase based on the knowledge defect of the incomplete entigen groupwith regards to the topic, wherein the second knowledge databaseincludes a second multitude of entigen groups associated with a secondmultitude of topics, wherein the second multitude of topics includes thetopic, wherein each entigen group of the second multitude of entigengroups includes a corresponding plurality of entigens of the secondmultitude of entigen groups and one or more entigen relationshipsbetween at least some of the corresponding plurality of entigens of thesecond multitude of entigen groups; and a fifth memory element thatstores operational instructions that, when executed by the processingmodule, causes the processing module to: modify the incomplete entigengroup utilizing the additive entigen group to produce an updated entigengroup to provide a beneficial cure for the knowledge defect of theincomplete entigen group.
 14. The computer readable memory of claim 13further comprises: a sixth memory element that stores operationalinstructions that, when executed by the processing module, causes theprocessing module to: store the updated entigen group in the firstknowledge database to replace the incomplete entigen group; store theupdated entigen group in the second knowledge database to replace theadditive entigen group; and output a representation of the updatedentigen group with an indication of a curated status.
 15. The computerreadable memory of claim 13 further comprises: a seventh memory elementthat stores operational instructions that, when executed by theprocessing module, causes the processing module to: determine theknowledge defect of the incomplete entigen group with regards to thetopic by one or more of: determining that a number of entigens of theincomplete entigen group is less than a minimum number of entigensthreshold number; determining that the incomplete entigen group does notcontain an expected yet missing entigen of an expected category; anddetermining that the incomplete entigen group does not contain anexpected yet missing entigen relationship between first and secondentigens of the incomplete entigen group.
 16. The computer readablememory of claim 13, wherein the processing module functions to executethe operational instructions stored by the third memory element to causethe processing module to recover the incomplete entigen group for thetopic from the first knowledge database based on the knowledge defect ofthe incomplete entigen group with regards to the topic by: obtaining asubject entigen group from the first knowledge database that includes atleast some of the plurality of baseline entigens of the baseline entigengroup; identifying a knowledge defect of the subject entigen group; andestablishing the subject entigen group as the incomplete entigen groupand the knowledge defect of the subject entigen group as the knowledgedefect of the incomplete entigen group.
 17. The computer readable memoryof claim 13, wherein the processing module functions to execute theoperational instructions stored by the fourth memory element to causethe processing module to obtain the additive entigen group from thesecond knowledge database based on the knowledge defect of theincomplete entigen group with regards to the topic by: identifying atleast one of a missing entigen and a missing entigen relationship of theknowledge defect of the incomplete entigen group; obtaining a candidateentigen group from the second knowledge database that includes at leastsome of the plurality of incomplete entigens of the incomplete entigengroup; determining that the candidate entigen group further includes asolution for the at least one of the missing entigen and the missingentigen relationship of the knowledge defect of the incomplete entigengroup; and establishing the candidate entigen group as the additiveentigen group.
 18. The computer readable memory of claim 13, wherein theprocessing module functions to execute the operational instructionsstored by the fifth memory element to cause the processing module tomodify the incomplete entigen group utilizing the additive entigen groupto produce the updated entigen group by: identifying at least one of amissing entigen and a missing entigen relationship of the knowledgedefect of the incomplete entigen group; extracting a solution for the atleast one of the missing entigen and the missing entigen relationship ofthe knowledge defect of the incomplete entigen group from the additiveentigen group; and supplementing the incomplete entigen group with thesolution to produce the updated entigen group.